From 0a1ec3930fbdaf7e30d731f05b22ed8d7c4755f1 Mon Sep 17 00:00:00 2001
From: Tim Swena <swast@google.com>
Date: Thu, 13 Feb 2025 13:21:41 -0600
Subject: [PATCH 1/6] feat: add `GroupBy.__iter__`

---
 bigframes/core/groupby/__init__.py            |  6 +-
 .../pandas/core/groupby/__init__.py           | 65 ++++++++++++++++++-
 2 files changed, 68 insertions(+), 3 deletions(-)

diff --git a/bigframes/core/groupby/__init__.py b/bigframes/core/groupby/__init__.py
index dfbe2ddea2..c6a026f6b4 100644
--- a/bigframes/core/groupby/__init__.py
+++ b/bigframes/core/groupby/__init__.py
@@ -15,7 +15,7 @@
 from __future__ import annotations
 
 import typing
-from typing import Sequence, Union
+from typing import Iterable, Sequence, Tuple, Union
 
 import bigframes_vendored.constants as constants
 import bigframes_vendored.pandas.core.groupby as vendored_pandas_groupby
@@ -142,6 +142,10 @@ def head(self, n: int = 5) -> df.DataFrame:
             )
         )
 
+    def __iter__(self) -> Iterable[Tuple[blocks.Label, pd.DataFrame]]:
+        # TODO: make a struct of all columns and then array_agg that.
+        return ()
+
     def size(self) -> typing.Union[df.DataFrame, series.Series]:
         agg_block, _ = self._block.aggregate_size(
             by_column_ids=self._by_col_ids,
diff --git a/third_party/bigframes_vendored/pandas/core/groupby/__init__.py b/third_party/bigframes_vendored/pandas/core/groupby/__init__.py
index 1e30d827ca..821d1d1749 100644
--- a/third_party/bigframes_vendored/pandas/core/groupby/__init__.py
+++ b/third_party/bigframes_vendored/pandas/core/groupby/__init__.py
@@ -1032,11 +1032,11 @@ def size(self):
 
         **Examples:**
 
-        For SeriesGroupBy:
-
             >>> import bigframes.pandas as bpd
             >>> bpd.options.display.progress_bar = None
 
+        For SeriesGroupBy:
+
             >>> lst = ['a', 'a', 'b']
             >>> ser = bpd.Series([1, 2, 3], index=lst)
             >>> ser
@@ -1074,6 +1074,67 @@ def size(self):
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def __iter__(self):
+        """
+        Groupby iterator.
+
+        This method provides an iterator over the groups created by the ``resample``
+        or ``groupby`` operation on the object. The method yields tuples where
+        the first element is the label (group key) corresponding to each group or
+        resampled bin, and the second element is the subset of the data that falls
+        within that group or bin.
+
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.display.progress_bar = None
+
+        For SeriesGroupBy:
+
+            >>> lst = ["a", "a", "b"]
+            >>> ser = bpd.Series([1, 2, 3], index=lst)
+            >>> ser
+            a    1
+            a    2
+            b    3
+            dtype: Int64
+            >>> for x, y in ser.groupby(level=0):
+            ...     print(f"{x}\\n{y}\\n")
+            a
+            a    1
+            a    2
+            dtype: Int64
+            b
+            b    3
+            dtype: Int64
+
+        For DataFrameGroupBy:
+
+            >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+            >>> df = bpd.DataFrame(data, columns=["a", "b", "c"])
+            >>> df
+               a  b  c
+            0  1  2  3
+            1  1  5  6
+            2  7  8  9
+            >>> for x, y in df.groupby(by=["a"]):
+            ...     print(f"{x}\\n{y}\\n")
+            (1,)
+               a  b  c
+            0  1  2  3
+            1  1  5  6
+            (7,)
+               a  b  c
+            2  7  8  9
+
+
+        Returns:
+            Iterator of tuples:
+                Generator yielding a sequence of (``name``, downloaded
+                ``pandas.DataFrame`` or ``pandas.Series``) for each group.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
 
 class SeriesGroupBy(GroupBy):
     def agg(self, func):

From 1975c8a8f003eecab7c212b25d0ad5ed85645821 Mon Sep 17 00:00:00 2001
From: Tim Swena <swast@google.com>
Date: Fri, 14 Feb 2025 16:03:57 -0600
Subject: [PATCH 2/6] iterate over keys

---
 bigframes/core/groupby/__init__.py            |  27 +++-
 .../bq_dataframes_covid_line_graphs.ipynb     | 137 +++++++++++++++---
 2 files changed, 139 insertions(+), 25 deletions(-)

diff --git a/bigframes/core/groupby/__init__.py b/bigframes/core/groupby/__init__.py
index 964fc28956..db645a7c9e 100644
--- a/bigframes/core/groupby/__init__.py
+++ b/bigframes/core/groupby/__init__.py
@@ -144,8 +144,31 @@ def head(self, n: int = 5) -> df.DataFrame:
         )
 
     def __iter__(self) -> Iterable[Tuple[blocks.Label, pd.DataFrame]]:
-        # TODO: make a struct of all columns and then array_agg that.
-        return ()
+        # TODO: cache original block, clustered by column ids
+        block = self._block.set_index(
+            self._by_col_ids,
+            # TODO: do we need to keep the original index?
+            drop=False,
+            index_labels=self._block._get_labels_for_columns(
+                self._by_col_ids
+            ).to_list(),
+        )
+        block.cached(force=True)
+
+        keys_block, _ = block.aggregate(
+            by_column_ids=self._by_col_ids,
+            dropna=self._dropna,
+        )
+        for batch in keys_block.to_pandas_batches():
+            for key in batch.index:
+                # group_block = block
+                # for col in self._by_col_ids:  # TODO: can't loop through key if only one by_col_id.
+
+                #
+                #  = block.project_expr(bigframes.core.expression.const(key, dtype=self._block._column_type(self._by_col_ids))
+                #     ops.eq_op( ex.const(key)
+                # )
+                yield key, batch  # TODO: filter clustered block by row
 
     def size(self) -> typing.Union[df.DataFrame, series.Series]:
         agg_block, _ = self._block.aggregate_size(
diff --git a/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb b/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb
index c3b4c8e616..6dccb39e5f 100644
--- a/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb
+++ b/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb
@@ -206,7 +206,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 1,
       "metadata": {
         "id": "R7STCS8xB5d2"
       },
@@ -216,13 +216,41 @@
         "\n",
         "# Note: The project option is not required in all environments.\n",
         "# On BigQuery Studio, the project ID is automatically detected.\n",
-        "bpd.options.bigquery.project = PROJECT_ID\n",
+        "# bpd.options.bigquery.project = PROJECT_ID\n",
         "\n",
         "# Note: The location option is not required.\n",
         "# It defaults to the location of the first table or query\n",
         "# passed to read_gbq(). For APIs where a location can't be\n",
         "# auto-detected, the location defaults to the \"US\" location.\n",
-        "bpd.options.bigquery.location = REGION"
+        "# bpd.options.bigquery.location = REGION"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "> **⚠ Important**\n",
+        ">\n",
+        "> You'll use features that are currently in [preview](https://cloud.google.com/blog/products/gcp/google-cloud-gets-simplified-product-launch-stages): `ordering_mode=\"partial\"` and \"NULL\" indexes. There may be breaking changes to this functionality in future versions of the BigQuery DataFrames package"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "\n",
+        "import bigframes.exceptions\n",
+        "\n",
+        "# Preview feature warning:\n",
+        "# Use `ordering_mode=\"partial\"` for more efficient query generation, but\n",
+        "# some pandas-compatible methods may not be possible without a total ordering.\n",
+        "bpd.options.bigquery.ordering_mode = \"partial\"\n",
+        "\n",
+        "warnings.simplefilter(\"ignore\", category=bigframes.exceptions.NullIndexPreviewWarning)\n",
+        "warnings.simplefilter(\"ignore\", category=bigframes.exceptions.OrderingModePartialPreviewWarning)"
       ]
     },
     {
@@ -236,11 +264,24 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 3,
       "metadata": {
         "id": "zDSwoBo1CU3G"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "Query job d9beb87c-89b3-44c6-9e99-082df80a4e95 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:d9beb87c-89b3-44c6-9e99-082df80a4e95&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "all_data = bpd.read_gbq(\"bigquery-public-data.covid19_open_data.covid19_open_data\")"
       ]
@@ -256,7 +297,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 4,
       "metadata": {
         "id": "UjMT_qhjf8Fu"
       },
@@ -276,7 +317,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 5,
       "metadata": {
         "id": "IaoUf57ZwrJ8"
       },
@@ -306,7 +347,56 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "Query job 33038dc8-ab4d-4940-a252-267215241412 is DONE. 372.9 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:33038dc8-ab4d-4940-a252-267215241412&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 68f3e706-1054-49a7-8b57-7d546b621ebf is DONE. 26.0 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:68f3e706-1054-49a7-8b57-7d546b621ebf&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2020-01-01\n",
+            "Empty DataFrame\n",
+            "Columns: []\n",
+            "Index: [2020-01-01, 2020-01-02, 2020-01-03, 2020-01-04, 2020-01-05, 2020-01-06, 2020-01-07, 2020-01-08, 2020-01-09, 2020-01-10, 2020-01-11, 2020-01-12, 2020-01-13, 2020-01-14, 2020-01-15, 2020-01-16, 2020-01-17, 2020-01-18, 2020-01-19, 2020-01-20, 2020-01-21, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26, 2020-01-27, 2020-01-28, 2020-01-29, 2020-01-30, 2020-01-31, 2020-02-01, 2020-02-02, 2020-02-03, 2020-02-04, 2020-02-05, 2020-02-06, 2020-02-07, 2020-02-08, 2020-02-09, 2020-02-10, 2020-02-11, 2020-02-12, 2020-02-13, 2020-02-14, 2020-02-15, 2020-02-16, 2020-02-17, 2020-02-18, 2020-02-19, 2020-02-20, 2020-02-21, 2020-02-22, 2020-02-23, 2020-02-24, 2020-02-25, 2020-02-26, 2020-02-27, 2020-02-28, 2020-02-29, 2020-03-01, 2020-03-02, 2020-03-03, 2020-03-04, 2020-03-05, 2020-03-06, 2020-03-07, 2020-03-08, 2020-03-09, 2020-03-10, 2020-03-11, 2020-03-12, 2020-03-13, 2020-03-14, 2020-03-15, 2020-03-16, 2020-03-17, 2020-03-18, 2020-03-19, 2020-03-20, 2020-03-21, 2020-03-22, 2020-03-23, 2020-03-24, 2020-03-25, 2020-03-26, 2020-03-27, 2020-03-28, 2020-03-29, 2020-03-30, 2020-03-31, 2020-04-01, 2020-04-02, 2020-04-03, 2020-04-04, 2020-04-05, 2020-04-06, 2020-04-07, 2020-04-08, 2020-04-09, ...]\n",
+            "\n",
+            "[991 rows x 0 columns]\n"
+          ]
+        }
+      ],
+      "source": [
+        "for key, group in usa_data.groupby(\"date\"):\n",
+        "    print(key)\n",
+        "    print(group)\n",
+        "    break"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
       "metadata": {
         "id": "tYDoaKgJChiq"
       },
@@ -336,7 +426,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 8,
       "metadata": {
         "id": "gFbCgfFC2gHw"
       },
@@ -344,7 +434,7 @@
         {
           "data": {
             "text/html": [
-              "Query job 307ec006-490f-435d-b3e3-74eb1d73fe0f is DONE. 372.9 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:307ec006-490f-435d-b3e3-74eb1d73fe0f&page=queryresults\">Open Job</a>"
+              "Query job 71d36657-d097-4421-831e-61b84dfe8f38 is DONE. 372.9 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:71d36657-d097-4421-831e-61b84dfe8f38&page=queryresults\">Open Job</a>"
             ],
             "text/plain": [
               "<IPython.core.display.HTML object>"
@@ -359,13 +449,13 @@
               "<Axes: xlabel='date', ylabel='New Cases'>"
             ]
           },
-          "execution_count": 9,
+          "execution_count": 8,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -419,7 +509,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 9,
       "metadata": {
         "id": "LqqHzjty8jk0"
       },
@@ -439,7 +529,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 10,
       "metadata": {
         "id": "g4MeM8Oe9Q6X"
       },
@@ -479,7 +569,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 11,
       "metadata": {
         "id": "x95ZgBkyDMP4"
       },
@@ -500,7 +590,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 12,
       "metadata": {
         "id": "V0OK02D7PJSL"
       },
@@ -508,7 +598,7 @@
         {
           "data": {
             "text/html": [
-              "Query job 44159a16-cab9-4ffa-be68-2228387a48c2 is DONE. 12.6 GB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:44159a16-cab9-4ffa-be68-2228387a48c2&page=queryresults\">Open Job</a>"
+              "Query job ee389eb5-10df-4b72-9baf-dd542851e3c8 is DONE. 129.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:ee389eb5-10df-4b72-9baf-dd542851e3c8&page=queryresults\">Open Job</a>"
             ],
             "text/plain": [
               "<IPython.core.display.HTML object>"
@@ -562,7 +652,7 @@
         },
         {
           "data": {
-            "image/png": 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GAEBSUpLf7e+++y5SUlIwcuRILFmyBDabrdX7cDqdMJlMfn8I8TpQbsTxagu8y71ugcEliHAL0g2MAcerLThQbozgKAkhJDIsTg/KGu2oMztjJpABQpyZqaurw8MPP4wnn3zS7/ZnnnkGp0+fxubNm/H000/jT3/6E+bOnduu+xRFEQ8++CAuvvhijBw50nf7TTfdhL59+yIrKwv79+/H7373Oxw7dqzVHVPLli3D0qVLQ3lapBcoPtMElyCCATg/f827FOwSRBSfacLonITuHh4hhESE1elBo80Flyd2ApiWOBZCsRiDwYA9e/YEFM4rKSnB2LFjYTQacfToUVx00UUwm83tus/77rsPX3zxBb777jv06dOn1fO+/vprXH755SgpKUFeXl7AcafTCafT6fvZZDIhJycHRqMRer2+nc+Q9FSrtp/C0k8OX/C8p68ejl9e3L8bRkQIIZFjdwlosLngdAsh30eiVonEuPBvzzaZTDAYDO26foc0M6NWq7Fjx46AYGbHjh1Qq9UApJkW798v5Ne//jU+/fRTfPPNN20GMgAwYcIEAGg1mFGpVFCpVO16XNL76FTte8u39zxCCIlFDreABqsLjk4EMdEkpE/s+++/H/feey/27NmDiy66CADwww8/4I033sDjjz8OANi0aRPGjBnT5v0wxnD//fdj3bp12Lp1K/r3v/A34eLiYgBAZmZmKEMnvZzV4QnreYQQEkscbgFNNjdsrp71GRdSMPPEE0+gf//+eOWVV/DOO+8AAIYMGYLXX38dN910EwDg3nvvxX333dfm/SxatAirV6/G+vXrER8fj6qqKgDSMpZGo0FpaSlWr16Nq666CsnJydi/fz8eeughTJ06Ffn5+aEMnfRyHMeB4wLzZfzPAXWCJ4T0KE6PFMRYnT0riPEKKWcmbA/eygVj1apVuO2223D27Fn8/Oc/x8GDB2G1WpGTk4P58+fjiSeeaHf+S0fW3EjPt+9sE657bSfcnuYk4BbHuOY/CjmP9++ZRAnAhJCY5xZENFpdsHRhEBOzOTPhcqE4KicnJ6D6LyGdMSrbgMHpOhwsN+H8dx+DFMwMTtdhVLYhyG8TQkhs8AgiGm1uWJyeXtEUOqRghuf5NqfhBaFnJBSRnofnOVwzOguHK0wQgvz3zXHANaOzqGAeISQmCSJDo80Fs6N3BDFeIQUz69at8/vZ7XZj7969ePvtt6nGC4lqosjwzYk6qOQ8HB4RLduM8BygksvwzYk63HHJAApoCCExQ2jRBLI3BTFeIQUzwQrhXXvttRgxYgT++9//4o477uj0wAjpCocqTDhcYYS7OYpRyDjf+pIgMrhFEYcrjDhUYcKoPrTURAiJbtHcybo7hdTOoDUTJ07Eli1bwnmXhIRVndUJk8MDkTEoZTzkPA958/8rZTzE5sZqdVbnhe+MEEIihDEGo82Ns402NNpcvTqQAcKYAGy32/HSSy8hOzs7XHdJSNg1Wd0QRQaeP9dc0ts1m+cAnuMgigxNVnekh0oIIQFiqZN1dwopmElMTPRLAGaMwWw2Q6vV4n//93/DNjhCwi1RqwDPc77GksEoZBwStYpuHBUhhFyYyeGG0eaGW6Ag5nwhBTMrVqzw+5nneaSmpmLChAlITEwMx7gI6RLJOhUUFwpmeA7JOmqJQQiJDhanB41WFwUxbQgpmLn11lvDPQ5CusWQNB0cF+gK6/CIGJKm66YREUJIcLHeybo7hZwz09TUhDfffBNHjhwBAIwYMQK33347DAbaAUKi1ycHKv22YwcjMum8hWPbbnpKCCFdIRydrHubkHYz/fjjj8jLy8MLL7yAhoYGNDQ04O9//zvy8vJQVFQU7jESEjZ7zzaG9TxCCAkXh1tARZMdlUY7BTIdFNLMzEMPPYRrrrkGr7/+OuRy6S48Hg/uvPNOPPjgg/jmm2/COkhCwkWjkIX1PEII6aye2sm6O4UUzPz4449+gQwAyOVyPProoxg3blzYBkdIuA1Jjw/reYQQEiqXR0SjzdVjO1l3p5CWmfR6Pc6cORNw+9mzZxEfTxcBEr2S41W4UJcCnpPOI4SQruAWRNSYHShrtPWIQOZAuRF7Tkd2aT6kmZnrr78ed9xxB55//nlMnjwZALB9+3b89re/xY033hjWARISTilxKiRqFWiwugO6ZgNS1+xErQIpcRTMEELCqyd1svYIIrYdr8PavWU4UmnG2L6J+Oi+yREbT0jBzPPPPw+O43DLLbfA45GiSoVCgfvuuw/Lly8P6wAJCacRWXroNQrUt1LhlwHQaxQYkaXv3oERQnosQWRosrlg6gGdrJtsLny6vxLr91Wg3uLy3b7ndCP2nW3C6JyEiIwrpGBGqVTixRdfxLJly1BaWgoAyMvLg1arDevgCAk3UWSoNbfdd6nW7PS1PCCEkFB5O1mbekATyNJaC9YWleOrI9VBi45e1C8RQgSfY0jBjNFohCAISEpKwqhRo3y3NzQ0QC6XQ6+nb7UkOn2yvxJWZ9tbHq1OAZ/sr8T8QuozRgjpuJ7SyVoQGXaW1mPt3jIUnzUGHFfIOFw6JA23Te6HyQNTIjDCc0IKZm644QZcffXV+NWvfuV3+/vvv48NGzbg888/D8vgCAm3sw2WoLkyLbHm8wghpCMYYzDZPWiyuyBcqDpnFLM4PfjiYBU+3luOSqMj4HiiVoFrRmfh6tFZSIpTIlGrjMAo/YUUzOzatQt///vfA26fPn06fv/733d6UIR0lWqT68IndeA8QgjpKZ2szzbYsG5vOTYdqoY9SNG+wek6LCjsg+mDU6GUh7QZusuEFMw4nU5f4m9Lbrcbdru904MipKuk69u3S6m95xFCerdY72TNGMOPpxuxtqgcu041BBznOeCSQSm4trAPRmTpwXHRmUsYUjAzfvx4/Otf/8LLL7/sd/s///lPjB07NiwDI6Qr5CTFhfU8QkjvFOudrO1uAV8ersa6onKcbrAFHI9XyzFnVCbmjslCul4dgRF2TEjBzDPPPIMZM2Zg3759uPzyywEAW7ZswQ8//IDNmzeHdYCEhNPs4el4qJ3nEULI+WK9k3W1yYH1xRX47EAlzI7AFZa+SVosKMzGjOHpMdXWJaRg5uKLL8bOnTvx3HPP4f3334dGo0F+fj7efPNNDBo0KNxjJCRs1u8vb/d514/v28WjIYTEiljuZM0Yw8FyEz7aW4bvTtQhWG7yxAFJWFCQjbF9E6N2KaktIQUzADBmzBi8++67bZ6zfPly3HvvvUhISAj1YQgJq3d3BbbhCOa1b05SMEMIgcMtoNHmgt0Ve0GMyyNi67EafFRUjhM1gTs0NQoZZo3MwPyCLPRJjO06cSEHM+3xl7/8Bddddx0FMyRq1FvaLpjndarOhh0ldRGvnUAIiQynR0CjNTY7WTdYXdiwrwKf7KtAoy2w2nmmQY35BdmYNTIDOlWXhgHdpkufRayXbSY9D8e17z3JACz74gjWL7qEKgET0ovEcifr49VmfFRUjv87WgNPkLWkMTkJWFiYjYkDkiHrYZ9rPSMkI6SdZHz7ayMcr7bgQLkxYr1GCCHdxy1IQYwlSFJsNBNEhu9K6vDRnjIcrDAFHFfIOFwxLB3zC7ORl6qLwAi7BwUzpFfhOvBtxCWIKD4TucZphJCuF6udrE12Nz47UIn1xRWoCdJvLlmnxLwxWfjZqCwYtIoIjLB7UTBDepXRWQb8VNe+wo6M0VIpIT1VrHay/qneinVF5dh8uBrOINvDh2XGY2FhH0wdlAK5LLqq9HYlCmZIrzJxYArW769q9/nxmp7/jYaQ3iQWO1mLjGH3qQZ8VFSOPacbA47LeA7TBqdiYWE2hmX2zkbPXRrMTJkyBRqNpisfgpAOsdoDM/tbwwFI0kW+gRohpPNisZO1zeXBxoPV+Li4HGWNgTPKBo0CP8vPxDWjs5Aa37tbsIQ0B1VUVIQDBw74fl6/fj3mzZuHxx9/HC7XuQZ9n3/+OTIzM1u9n2XLluGiiy5CfHw80tLSMG/ePBw7dszvHIfDgUWLFiE5ORk6nQ4LFy5EdXV1KMMmBN+V1rf7XAagLEiZb0JI7GCMwWhz42yjDY02V0wEMhVNdvxjawmuf+17vPJ/JQGBzIDUOPz2ysFYc9cE3HFJ/14fyAAhBjP33HMPjh8/DgA4efIkbrjhBmi1WnzwwQd49NFH230/27Ztw6JFi/D999/jyy+/hNvtxpVXXgmr1eo756GHHsInn3yCDz74ANu2bUNFRQUWLFgQyrAJgdnR/pkZAHjl6xPYUVLXRaMhhHQVxqSZmLMNdtRbnRCClb2NIowx7D3TiCc/PohfvLkbH+4ph7VFoT4OwMUDk/H360bj9V+MxexRmVDFULuBrsaxEDKfDAYDioqKkJeXh2effRZff/01Nm3ahO3bt+OGG27A2bNnQxpMbW0t0tLSsG3bNkydOhVGoxGpqalYvXo1rr32WgDA0aNHMWzYMOzcuRMTJ0684H2aTCYYDAYYjUbo9b1zLZGcM/+Vb7G3LHD7YmvUCh4X9UvC278cT/VmCIkRZocbTTHSydrpFrDlaA3W7i3HyVprwPE4lQxXjczEvIIsZBqiM20jUatEYlz4l+Q7cv0OKWeGMQZRlN4kX331FX72s58BAHJyclBXF/q3WKPRCABISkoCAOzZswdutxszZszwnTN06FDk5ua2Gsw4nU44nee2qZlM7b9wkZ6vqQM5MwCglPEorbHgUIUJo/oYumhUhJBwiKVO1rVmp69KrylIbZs+iRosKMjGzBEZ0ChpBuZCQgpmxo0b5+ucvW3bNqxcuRIAcOrUKaSnh9ZtWBRFPPjgg7j44osxcuRIAEBVVRWUSmVAO4T09HRUVQXfkbJs2TIsXbo0pDGQnq/R6rrwSS3EqWRwiwwNto79HiGk+9hcHjRYY6OT9eEKEz4qKsM3J+qCLn1d1C8RCwqzcVG/JPAx2PAxUkIKZlasWIGbb74ZH3/8MX7/+99j4MCBAIAPP/wQkydPDmkgixYtwsGDB/Hdd9+F9PteS5YsweLFi30/m0wm5OTkdOo+Sc/R0aUiHoCC55CkpV1NhESbWOlk7RZEfHO8Fh8VleNolTnguFrO48oRUsPHvslxERhhaDiOQ5xSBq0q8jNHIQUz+fn5fruZvJ577jnIZB1/Ur/+9a/x6aef4ptvvkGfPn18t2dkZMDlcqGpqclvdqa6uhoZGRlB70ulUkGlosxuEpxa3rGc9yaHB4W5iRiRRflWhESLWOlk3WRz4ZP9ldhQXIH6ILPCafEqzCvIxpxRGYhXx05NK7VCBp1aDp1SHjW5hGGtM6NWqzt0PmMM999/P9atW4etW7eif//+fsfHjh0LhUKBLVu2YOHChQCAY8eO4cyZM5g0aVLYxk16j45uaFDKONw3LS9q/oMlpDeLlU7WpTUWfFRUji1Hq+EWAj908vsYsKAwGxfnpcRMw0eFjIdOJYdOLYciCisLtzuYSUxMBNfO9buGhoZ2nbdo0SKsXr0a69evR3x8vC8PxmAwQKPRwGAw4I477sDixYuRlJQEvV6P+++/H5MmTWrXTiZCzuf0dOyb3KVD0jB5YEoXjYYQ0h6x0MlaEBl2lNZjbVEZ9pUZA44rZBwuG5qGBQXZGJQeH4ERdhzPcYhTyRGvlkMd5dvA2x3MrFixwvf3+vp6PPPMM5g5c6ZvhmTnzp3YtGkTnnzyyXY/uDdxePr06X63r1q1CrfddhsA4IUXXgDP81i4cCGcTidmzpyJf/zjH+1+DEJaCtbLpC0//tSAHSV1FNAQEgGx0Mna4vDgi4OVWLe3AlUmR8DxpDglrhmdiZ/lZyGpC7YvdwWtUpqBiVPK2j2JEWkh1ZlZuHAhLr30Uvz617/2u/2VV17BV199hY8//jhc4+s0qjNDWhrw2GfoSDij4IDsJC3+Mn8UBTSEdJNY6GR9psGGdXvLselQFRzuwE+Vwek6LCzsg+lDUqNyWeZ8SjmPeJUCcSpZ1DSo7PI6M5s2bcKzzz4bcPusWbPw2GOPhXKXhHSLjm7cFBhgdniwclspJg5IptwZQrpQtHeyZozhx9ON+KioHLtPBaZT8BwwZZDU8HFElj7qZzVkPOfLg1HJo3sZ6UJCCmaSk5Oxfv16PPzww363r1+/HsnJyWEZGCHRQASgUcqocB4hXUgUGZqiuJO13S1g86FqrNtbjjNB+rXFq+WYMyoT88ZkIU3fsY0w3c27nVqnlkOjiJ1lpAsJKZhZunQp7rzzTmzduhUTJkwAAOzatQsbN27E66+/HtYBEhJpHGNUOI+QLiCKDKbm1gPRGMRUmRxYv7ccnx2ogiVI8nHfZC0WFmZjxrD0qE+QVSlkiI+y7dThFFIwc9ttt2HYsGF46aWXsHbtWgDAsGHD8N133/mCG0J6CrPTA61SToXzCAkTxhhMdg+a7K6oawDJGMOBciPWFpXju5K6gHIOHIAJA5KwsLAPCnMTonpmQyHjEaeSQ6eSQ9nBGluxJuQ6MxMmTMC7774bzrEQEpXsLhGj+uiocB4hncQYg8nhgdHmhkeMrtYDLo+I/ztWg4+KylFSYwk4rlHIMHtkBuYXZCM7MTobPgLSdmqtSoZ4laJX9XQKOZgRRRElJSWoqanxNZ30mjp1aqcHRki0UCl4KpxHSCdFayfrBqsLG4or8Mn+CjTaAhvRZhrUmF+QjVkjM6BThbXObFhplDIpmVclj+rZoq4S0r/M999/j5tuugmnT58OyDjnOA6CEN0lpgnpiIevHEzbsgkJUbR2sj5ebcaHe8qw9VgtPEGWugpyE7CgIBsTByRHbZVehYyX8mBU8qjZTh0pIQUz9957L8aNG4fPPvsMmZmZvTIKJL0DB+Dm8X0jPQxCYk40drIWRIZvT9RhbVEZDlaYAo4r5TxmDJOq9A5I1UVghBcm4zlfHky0Jx13p5CCmRMnTuDDDz/0dcsmpKdiAA5VmjA6JyHSQyEkJkRjJ2uT3Y3PDlRifXEFaszOgOMpOiXmjcnGnFGZMGijr+Ejx3HQNi8jaWOoKm93CimYmTBhAkpKSiiYIb3Cjz/VUzBDyAVEYyfrU3VWrNtbji8PVwdtZTI8Mx4LCvtg6qCUqFymUSnO5cFE61JXtAgpmLn//vvx8MMPo6qqCqNGjYJC4R/J5ufnh2VwhESDb0vqcceUvEgPg5CoFG2drEXGsPtUAz4qKsee040Bx2U8h+mDU7GgMBvDMqNvh6Kc56FT947t1OEUUjCzcOFCAMDtt9/uu43jODDGKAGY9Dh8dJXBICQquDwimmyuoMXkIsHm8mDjQalKb3mTPeC4QaPA1aMzcc3oLKToVBEYYet663bqcAopmDl16lS4x0FI1MrPoRYGhHhFWyfriiY71u0tx8aDVbAGWeLKS43DgsI+uHxoWtTNdHi3U8f10Kq83SmkYKZvX9rdQXqPKYNTIz0EQiIumjpZM8aw92wT1haVY2dpPc4fDc8Bk/NSsKAwG6P7GKIqYZa2U3eNkCsAvfPOO/jnP/+JU6dOYefOnejbty9WrFiB/v37Y+7cueEcIyERZYqSb6CEREI0dbJ2ugVsOVqDtUXlOFlnDTgep5LhqpGZmFeQhUxD9FTplfEctEo54tW0nbqrhBTMrFy5Ek899RQefPBB/PnPf/blyCQkJGDFihUUzJAepckaWBWUkJ4umjpZ15qdWF9cjk/3Vwb9ctEnUYMFBdmYOSIjanJOOI6DRiF1p46j7dRdLqRg5uWXX8brr7+OefPmYfny5b7bx40bh0ceeSRsgyMkGlQabZEeAiHdJlo6WTPGcLjShLVF5dh2vDag4SMAjO+XiAWFfTCuXyL4KAkWlHIe8SoFdGraTt2dQk4ALigoCLhdpVLBag2c+iMklm0+XIN7pw2kBD3So0VLJ2u3IGLb8Vp8VFSOY1XmgONqOY+ZI6SGj7nJ2giMMJCc5xGnkmZhVPLomBnqbUIKZvr374/i4uKAROCNGzdi2LBhYRkYIdGiqsmGQxUmjOpDu5pIz8MYg9npQZM1sp2sG20ufLqvEhv2VaDe6go4nq5XYX5BNq4amQmdOvINHzmOQ5xSCmC0ysiPp7cL6V9g8eLFWLRoERwOBxhj2L17N9577z0sW7YMb7zxRrjHSEhEOQSGBlvghyshsS4aOlmX1ljwUVE5thythlsInBHK72PAgsJsXJyXEhXLNurmPBgdbaeOKiEFM3feeSc0Gg2eeOIJ2Gw23HTTTcjKysKLL76IG264IdxjJCSiBIEhSauM9DAICZtId7IWRIYdpfVYW1SGfWXGgOMKGYfLhqZhYWEfDEyLfMNHhYyX2gqo5VDQduqo1OFgxuPxYPXq1Zg5cyZuvvlm2Gw2WCwWpKWldcX4CIk4tyBiWEZ8pIdBSKdFupO1xeHB5wcr8fHeClSZHAHHk+KUmDs6Cz8bnYnECH+B4DmpOzVtp44NHQ5m5HI57r33Xhw5cgQAoNVqodVGRxIWIV3B4RGx5sez+PlEKhZJYpPdJTWBdESok/WZBhvWFZVj0+EqONyBgdSQjHgsLMzGtMGpEZ/50CrltJ06BoW0zDR+/Hjs3buXKgGTXmP1rtO4aXwueJ6DKDIcqjChweZCklaJEVl6WjsnUSmSnaxFxvDjT41YW1SG3T8FNnzkOWDa4FTML8jGiCx9RAMH73bqOJWMqvLGqJCCmV/96ld4+OGHUVZWhrFjxyIuLs7vOHXNJj1NRZMDhypMMDvc+MfWEhytMsPtYVDIOQzNiMevpg/E5IEpkR4mIQAi28na7haw+ZDU8PFMQ2CNJr1ajjn5mZg7OgtpenW3j89LxnO+PBjaTh37OBZCfWqeD4xco7VrtslkgsFggNFohF4ffe3eSffq99hnIf2eVinD/ZcNwls7TqHB6kLL/2o4Tlrrf+G6MRTQkIiKZCfrKpMDH+8tx+cHqoI+fr9kLRYU9sGMYWkRy0FpuZ1ao6BlpGjXkes3dc0mpB1cHgEf7jmLWrMTHCcVyeIAMAAeUUSt2YllXxzB+kWX0JIT6XaR6mTNGMP+ciPWFpVje0ldQJVeDsDEAclYWJiNgtyEiAUPKoXUnVqnoqq8PVVIwczp06cxefJkyOX+v+7xeLBjxw7KpSE9jihKSYwcAAXP+z6UvT+7BRHHqiw4UG7E6JyESA6V9CKR6mTt8oj4urnhY0mtJeC4VinDrOYqvdmJkWn4KOd5qR6MSg6lnPJgerqQgplLL70UlZWVAduxjUYjLr300qhaZiIkHBgAjyDlyJz/7ZLjOMhkHDyCiL1nmyiYIV0uUp2sG6wubCiuwIZ9FWiyBzZgzUpQY35BNmaNyECcqvur4vIcB61KhniVImoaTpLuEdK7zZsbc776+vqAZGBCegLv5UIUGWTBPiObT+Ai21yY9HCR6mR9rMqMj4rKsPVYLTxB+jYV5iZgQWE2JvRPjsgyjkYpLSPFUVXeXqtDwcyCBQsASN9Eb7vtNqhUKt8xQRCwf/9+TJ48ObwjJCQKKHjALQIeEeB5EWAcOE5aZgInfVNWyHiMyU2I8EhJT+TtZG20u7utCaQgMnx7Qmr4eKjCFHBcKedxxbB0LCjMRv+U7v8Sq5DxiG9eRqLt1KRDwYzBIDXaY4whPj4eGs25tVClUomJEyfirrvuavf9ffPNN3juueewZ88eVFZWYt26dZg3b57v+G233Ya3337b73dmzpyJjRs3dmTYhHSajOcggkEQAZeHwTsVw+FcQDM4XYdR2dSMkoRPJDpZG+1ufLa/EuuLK1BrcQYcT9WpMHdMFubkZ8KgUXTLmLxkvFSVV6eiqrzEX4eCmVWrVgEA+vXrh0ceeeSCS0rbt2/HuHHj/GZwWrJarRg9ejRuv/1236zP+WbNmuV7XACt3hchXcnhCX4h8YY1iRoFlsweRlPcJCwi0cn6VJ0Va4vK8dWRajiDtDsYkaXHwsJsXDIwpVtnQjiOg7Z5GUlLVXlJK0LKmXn66afbdd7s2bNRXFyMAQMGtHp89uzZbd6HSqVCRkZGu8fmdDrhdJ77NmEyBU6PEhIO3rhFxnPok6jBxAHJkR0Q6RG6s5O1yBh2nWzAR0VlKDrTFHBcznOYPiQVCwqzMTSje+t00XZq0hFdmm4ejiz7rVu3Ii0tDYmJibjsssvwzDPPIDm59YvGsmXLsHTp0k4/LiEXIjLpw17Oc74KwaP60DITCU13drK2Oj3YdKgK6/ZWoLzJHnA8QaPA1aMzcc3oLCTrum82nLZTk1B1/965Dpg1axYWLFiA/v37o7S0FI8//jhmz56NnTt3QhZ0SwmwZMkSLF682PezyWRCTk5Odw2Z9DoMLg+Dy+PCdyW1FMyQDuvOTtblTXas21uOjQerYAvSr2lgqg4LCrNx2dC0bgsmOI5DHG2nJp0U1cHMDTfc4Pv7qFGjkJ+fj7y8PGzduhWXX3550N9RqVSUV0O6jUcElHIOHoFh06Fq3DM1j/JmSLt0Vydrxhj2nmnCR0Xl+P5kPc6fL+c5YHJeChaOzUZ+tqHbclJoOzUJp6gOZs43YMAApKSkoKSkpNVghpDu5vEwqBQyVBvttNRELqi7Olk73QK+PFKDdXvLcarOGnA8TiXDVSMzMb8gGxmG7mn46N1OHaeSQ0HbqUkYdWkwE+4Iv6ysDPX19cjMzAzr/RLSGSKANJ0SNo+IBpsr0sMhUaq7OlnXmp1YX1yOT/dXwhSkV1NOogYLCrNx5fCMblnW4TlpO3W8mrZTk64T0QRgi8WCkpIS38+nTp1CcXExkpKSkJSUhKVLl2LhwoXIyMhAaWkpHn30UQwcOBAzZ87symET0mECAAXPIUmrjPRQSJTpjk7WjDEcqjBhbVE5vjlRG9DwEQDG90vEgsI+GNcvEXwXLyVxHAeNQupOHUfbqUk36NJgxmw2t3n8xx9/xKWXXur72Zu4e+utt2LlypXYv38/3n77bTQ1NSErKwtXXnkl/vSnP1FODAmJ2IVFx4w2N8bkJmBEVvduXyXRqzs6WbsFEVuP1WJtUTmOVQd+3qoVPGYOlxo+5iZru2wcXko5j3iVAjo1bacm3SukYKa6uhqPPPIItmzZgpqamoAZmPY2mpw+fXqbszebNm0KZXiEBBWsJHvYcAz3TaPkXyJ1sm6yu2HuwiaQjTYXPtlXgQ37KtFgDVzazNCrMa8gC1eNzIRO3bWpkXKeR5xKmoVRyWkZiURGSO/y2267DWfOnMGTTz6JzMxMmkIkMaEr81kcXZzMSaJfd3SyPlFtxtq95fj6aA3cQuBjjO5jwILCPpic17UNHzmOQ5xSCmC0ypjaR0J6qJDehd999x2+/fZbjBkzJszDIaTrdGU+i1sE/vL5EWz49SU0O9PLiCKD0S41geyKTtaCyLC9tA5ri8qxv8wYcFwh43D5UKnh48A0XdgfvyV1cx6MjrZTkygTUjCTk5PTZd88COkKosi65ELT0qEKE1bvPoOfT+zbpY9DokNXd7I2O9z4/EAVPi4uR7UpsOFjcpwS14zJwtX5mUjowkBdIeOltgJq2k5NoldIwcyKFSvw2GOP4bXXXkO/fv3CPCRCwmtHSR1WbitFSZAEyXBiAF75+gRuGp9L31p7sK7uZH2m3oa1e8ux+VAVHEGqAg/NiMfCwmxMHZzaZcEFbacmsabdwUxiYqJfbozVakVeXh60Wi0UCv828A0NDeEbISGdsKOkDo+vOwCL0wNNN3woV5uc2H+2CWP6Jnb5Y5Hu1ZWdrEXG8MNPDVhbVI4ffmoMOM5zwLTBqVhY2AfDu3DHnFYpp+3UJCa1O5hZsWJFFw6DkPATRYaV20phcXqQoVd3aZ0PLwbgi0NVXRbMiKJUT6TB5kKSVokRWXqaBeoGXdXJ2u4SsPlwFdYWleNsY2DDR71ajp/lZ2LumGykxndNSQrvduo4lQxyWkYiMardwcytt97aleMgJOwOVZhQWmNBolYJjuMg57vng9oahgqvwYKW70/WY+W2UpTWWOAWGBQyDnlpOtw3LQ+TB6aEYeTkfFan1AQy3EFMldGBdXvL8fnBSlidgTvh+qfEYUFBNmYMS4OqC2YUZTzny4Oh7dSkJwgpZ+bzzz+HTCYLqMS7efNmCIKA2bNnh2VwhHRGg80Ft8CgbP62qVZ0TzBTkNO5WRlvjk/LoCVZp0SN2QlBZEjUKqGU8XAJIo5UmvH4ugP4y/xRFNCEUVd0smaMYX+ZER8VlWNHaV1AlV4OwKS8ZCwozEZBTkLYl3k4joO2ubmjlpaRSA8TUjDz2GOPYfny5QG3i6KIxx57jIIZEhWStEooZBxcggg1330f3tXGwOWC9mqZ4+MNWpweAUerzBBEhtwkrS8hU83LkKHnUWVyYuW2UkwckExLTp3kcAtosIa3k7XLI2LL0RqsKypHSa0l4LhWKcPskRmYV5CN7ARN2B7XS6WQAhidiqrykp4rpGDmxIkTGD58eMDtQ4cO9eu1REgkjcjSIy9NhyOVZmTo+W4LZl76ugRDM/VIjVd3KKfl/Byfc+PlfP9bZ3FBp5aD897GcUjQKlBaY6GO3Z3QFZ2s6yxObNhXgU/3VaLJ7g44np2gwfyCLMwckYE4VXgLz8l5XqoHo5JDKac8GNLzhfRfkMFgwMmTJwO2ZZeUlCAuLi4c4yKk03iew33T8vD4ugOoMjmRoFVc+JfCwOER8Zv/FkMtlyFNr8KN43Nxw7gcHKkyt5m4e36Oj5dHFMGYlOfg9AhwuES/bscqGQ+jyKhjdwicHgFNNjesYUwOP1olNXzceqwWniBbt8fmJmBBYR9MGJAU1oaPPMdBq5IhXqXolm7YhESTkIKZuXPn4sEHH8S6deuQl5cHQApkHn74YVxzzTVhHSAhnTF5YAr+Mn+ULwelu9icHtidHtRZnHhq/UEs++II1HIePMe3mrh7fo6Pl5znwXEAOICJaN4WfO5i5RRE6tjdQeHuZO0RRHx7og4fFZXjcGVgDzCVnMcVw9MxvyAb/VPC+4VP05wHE0dVeUkvFlIw89e//hWzZs3C0KFD0adPHwBAWVkZpkyZgueffz6sAySksyYPTMHEAck4VGHC1a981y2PKTCpNgjPSX+3OgU43QKyE7RQyvmgibvn5/h4qRU8VHIedpcAjoPfrizGGJpsbgzLjMeILD1t3b4AtyCiyeaG2RG47BMKo92Nz/ZXYn1xBWotgVV6U3UqqeHjqEwYNOGbGVTIeMQ3LyPRdmpCOrHMtGPHDnz55ZfYt28fNBoN8vPzMXXq1HCPj5Cw4HmuW/NJOCBgt4rIgHqrE32TtTCo5aizOPHXTcfwYb8kyOV8qzk+HMchRafCmQabtCzBMYgig7P5wqxTyXDftDzaut0Gbydro82NE9UWGB0uGNRKDEyPC2mp51SdFR8VleGrIzVBdzyNyNJjYWEfTBmUErakWxkvVeXVqagqLyHn41gHmyy53W5oNBoUFxdj5MiRXTWusDGZTDAYDDAajdDru65yJokN/R77rFseh+MAMKmInpeC5yACUMp4eEQRYnO0M6pPAh6dOQSTB6a02M0kIEGrgErG+4IWGQ+kxatQb3HBLTIo+HPBCoCAXVAuQURjc7ATzVu3u3I2qWUn66LTDVi9+yzO1lt9r19OchxuGp+DgtwLb6cXGcP3J+uxtqgcRWeaAo7LeQ7Th0hVeodkxIdl/LSdmvRmHbl+d3hmRqFQIDc3F4IQvqx/QnqcIF8RGBgEEXAyAQoZD14GeETgZK3Vb8mpZY6PsfmiOywzHvdNy/Mtl7W88APArat2B+yCioWt28Fq6oRjNun8TtZ7zzTi718eh80lQK9WQC/j4BYYTtZa8Pcvj2PxFYNbDWisTg82HqrCur3lqGhyBBxP1CpwdX4Wrh6diWRdeKr00nZqQjompGWm3//+93j88cfxzjvvICkpKdxjIiTmMXg3VEs4SIELAMhlHHiOg8gAnmNI1SlhdHh8AUfLHJ9gsxXnL5cdKDMG3QUFRPfW7WA1dTpbCJCxc0GMtwmkyBhW7z4Lm0tAik7p29auknNI0SlRZ3Fh9e6zGJ2T4LfkVN5ox7q95dh4qAq2IFu2B6bpsLAwG5cOSQu6/VlkDCXV1nYvadF2akJCF1Iw88orr6CkpARZWVno27dvwHbsoqKisAyOkFjWcnKG4wDGpKCGBwfGGDwig0bBQ6OUgeM5v4CjIzk+re2C8orGrdut1dQJdTaJMQaTw4MmW2An65JqK87WW6FXK3yBjBcHDvFqBc7WW1FSbcWg9DgUnWnCR0Vl2HWyIWCCjeeASwamYEFhNkZlG1pd9tl7prFdS1ocxyFOKYNOLYdWGd5aM4T0JiH91zNv3rwwD4OQns17fZXzUpDjERlkHIfUeOlC3pmAo7VdUF7RuHW7tZo6QMdmk9rTydrokHKM9LLggYdSxsEkith8uArLNzbip3pbwDk6lRxXjZKq9Gbo1W0+t/YsaU3KS5FmYWg7NSFhEVIw8/TTT4d7HIT0WFoFDxnPw+r0QIQ0RaNR8EiNV0PXXPm1MwFHW5WOz9+6HS3CMZvU3k7WBrUSCl4KKFRy/8DBLYiot7pgdniwdm95wO/mJmkxvyAbV45Ih6YdO4jaWtJK1alQZ3Vh7d5yzC/oQ0EMIWFE85qEdBEOgErGYXCGHmvumIAb3tyFk7VWpOqU0tJSc9DR2YAjWKXjlrugvFu3o+ni2ZnZJKvTg0Zb+5tADkyPQ05yHE7WWpCiUwJMqtLcaHO3WjRvfP8kLCzMxti+iR3auh2wpMVJlXllvJQnlRSnxKlaa9TlLxES60IKZgRBwAsvvID3338fZ86cgcvl/+2poaEhLIMjJJYxAG4GnG2w4kSdFY/OHILH1x2A0eEBx3NhDTgutAsq2rZlhzKbZHN50GhzwxmkCWRbybY8x+Gm8Tn42+ZjKG9ywCMwuILM5qgVPGaOyMD8gmzkJmlDel7eJa0EOQ+5jAfPwe+5RTp/iYoqkp4qpGBm6dKleOONN/Dwww/jiSeewO9//3v89NNP+Pjjj/HUU0+Fe4yExCxBZGiye1BndeLSIWldGnBcaBeUVzRc0Doym3ShTtYXSrZtsLpwoNwIs1MIuispKU6J68f1weyRmdCpQ5+sVsp55CbGQS3nwYCgW6ojmb/UVdvgCYkGHS6aBwB5eXl46aWXMGfOHMTHx6O4uNh32/fff4/Vq1d3xVhDQkXzSEvdVTTvfH/7n9FYOFZq/eFyCfjnNydxusGKvklxuHfqACibGwN2daARbRc0v/GcVwiwsG/iBTtZn59sq2hOtjU53JDzHPomx2FfWRPcQuDH3OB0HW6ekIvJeaFX6ZXxnFQPRi2HSi6DKDLcump384yTKmDGqcrkxLDMeLz9y/HdGkC2tg0+Fooqkt6rS4vmAUBVVRVGjRoFANDpdDAajQCAn/3sZ3jyySdDuUtCerTisiYsHNsHr39Tile3lsJsd0MEwAP4945TWDQ9DyOyDF0aaHRFXZfOCjabNDAtDiaHBxVN9jZ/N1iyLWMMLo8Iu1uAwy2i1uK/nKOQcZgxLB0LCrKRl6YLacwtt1NrFP5VeaMxfynYNnjGGBgD4pQyNNnc+MfWkqgsqkhIe4UUzPTp0weVlZXIzc1FXl4eNm/ejMLCQvzwww9QqcJTAZOQnmT7iVq8tq0Uz206BkFkkMs4yDlpy7bR5sbyL45Cr1FAxnNdEmiEu65LOHlr6ng7WVcaA6vsBtMy2VYUAaPdhSa7G57zm2IBSNYpMXd0Fn6Wn4mEEJd41ApZu7ZTR1v+0vnb4C1OD2rNDjg9Irzz8rtPNWL17jP4+cS+3Tq2UEXDUimJLiEFM/Pnz8eWLVswYcIE3H///fj5z3+ON998E2fOnMFDDz0U7jESEhZikItcd6kzOfDK1yUQRAalnAPPSVuSOU4ak1tgaLS5MSw9DnK5tOQUzkAjXHVd2hLqBSbUTtZGhwsOjwiHR9paHexfV8ZzuG5cH9w2uR9kPIeSaitO1Fja3WRSIeN9y0iKDnSnbm/+UndouQ3e4vSgvNEOgTHIeQ4cB4hgcHtEvPz1CQxIiYv65aZoWyol0SGkYGb58uW+v19//fXIzc3Fzp07MWjQIFx99dVhGxwh4XSowhSxx7Z7RAhMbG5lIF0UBcbgEUS/7ton6+3IStD46s+0DDQOlBvBc1xIF8eurhIcygXG28na7PCgI6l7ImP44acGvLPzDEyO4Fur41VyaFUyMJFh2qA0HCw3BiYJJ2kxZXAqMg1qv+CG5zhoVTLo1YpOdafu7k7trfFug3d6BNSaHRCY9Px9XdkZIOMZnB4xant4eUXjUimJDmGpMzNp0iRMmjQpHHdFSJepNbdv+aIreERpq7ai+RohMOnb8PmXcJdHRHmjHdmJ5wIalYxHrUvA4+sOoNHq6vC3UVFkaLC4IIgiTA43DBpFwOxMZ3bZdPQC07KTdUeCGLtLwKZDVVi7txxljYH5NDwHJGgUMGgUkMs41FlcGJCqg9npwoqvTvhV5DU53NhX1oS9Z5ugVcqhVfDolxKHe6bm4bJhaT2qO7V3G/yBMiOcHrF5Rqa5xhEYBJFBrZAjRaeMyh5eXtG8VEoiL+RuZu+88w4uvvhiZGVl4fTp0wCAFStWYP369WEbXDQTRYYDZUZsO16LA2XGiC5hkAvbUVKH5RuPRezxve8Ot8DAIM3IeJtRtvzYlfFSoFNrdvgu9I12FywON840l9mPV0uzDt5gYUdJXauPu6OkDreu2o3nNh2F2elBeZMdp+qsfsXivHVd8tJ0HS7a573AmB1uGNQKuAURTo8IlZxHhl4Fi1PAym2lEEUGUWRotLpwtsEGo93d7kCm0mjHyq2luO5fO/HS1yUBgYyc56BVypCVoEFSnBICA+osLmiVMtxwUR+s+aHMlySskvNSwTyrW5oRY4AoiojXyHGyzoY/fXYYO0vrO/QaRDtvUrJKzsMjMin5FwwiY/AIDDzHITVeBZVMBncHZ+e683OwI0ulpPcJaWZm5cqVeOqpp/Dggw/iz3/+MwRB2jqZkJCAFStWYO7cue26n2+++QbPPfcc9uzZg8rKSqxbt86v7xNjDE8//TRef/11NDU14eKLL8bKlSsxaNCgUIYdNrRmG1u8MwcN1sg3WhSZNPvibTrZ3N3AR9oizMHpEeFwi1DJOVSbnBAZYHd7YHcL4DhAJZchRaf0BQvBvo2eP2OikPMob7TD5hJQ1mhDlkEDhZzv1C6bQxUmHK4wwu4SYXLYpOfFASq51K4hQatASbUZ35+sR3aiJqAJZGsYY9hXZsRHRWXYWVqP83+NAzA5LxkLCrPBGMN7P5ThbL0VVpcHCo7DgFQdbhqfgzil1EQyXiWH083gET2ot7p9Sy3gOLhFBg48MvTyHvvtfvLAFNx/+SD86dPDEEQRoiD9O6kVMqTGq6BTyWF3Cx2anevuz8FYbKhKuk9IwczLL7+M119/HfPmzfPLnxk3bhweeeSRdt+P1WrF6NGjcfvtt2PBggUBx//617/ipZdewttvv43+/fvjySefxMyZM3H48GGo1W03e+sqtGYbW1pOTafqVK3mWHQHDtIMjd+FucXfeQ4QRIDnpG2zVpcHlUYPBJGBByDjpWJsjDHYXQIqmuxIjVcHXRoINiWvVsjAJ3GoMTlgdwuoMNqREqds1y6b1pJ7vyupQ6PNDY4D5DwvdQcHYHeLKGuwIUOvhlNgONtoQ4bhwv/Nujwithypxtq95SittQYcj1PKMHtUBuaNyUZWgsZ3+5jcxKAVgH/4qQE2lwiz0wO3IEIUAdH7b8Fx4DmAiYBHFMFxsrAkQkerm8bnYuPBKhysMMKglkMhk0Gt5H1b2jvSUiMSn4Ox2FCVdJ+QgplTp06hoKAg4HaVSgWrNfADqDWzZ8/G7Nmzgx5jjGHFihV44oknfDM9//nPf5Ceno6PP/4YN9xwQyhD7xRas409LaemVYqQV1XDxhvQtCTjgNR4NTRKGWrNTthdHogA7E4PAAYO0oyNW5BmdLzLU6IA1FsciFcrfd9GvUHHnjONOFppRoLWPz9Gp5IjLjUORpsbNpeAR2cNw9wxWW2+X1v7Bn7P1AHYdKiq+Tlw53YGMQY5B7hFhhqLE3qVDAZ12xeYOosT64sr8On+ShjtgbuashM0mF+QjVkj06FVBn5s8RyHwRmBdWNqzU7Y3B6AAQo5Dw7SchcD4PZICdneQAzo2d/ueZ7Dr6bnNQchAhK0MjARcAhCh2bnIvU5OCJLjwGpcThYboJBo4BCxkOt4H11c6KxoSrpPiEFM/3790dxcTH69vWvSbBx40YMGzYsLAM7deoUqqqqMGPGDN9tBoMBEyZMwM6dO1sNZpxOJ5xOp+9nkyl866fdsb2VhJff1HSE05oYAIUMcAtAsk4Bu1NAnEoOrVIGhUwGgYlgTNrdxPMcGBicggg0Bwbn3xcAODwMKkFEklaJHSV1+MfWUhyrMsPqkpakbC4P0vTnunMDAAcOerUCDo+IRK2ize3DbX0D/+2H++HyiFDJZXALInhIkRYDAI6DjJcK2CWnxGFgelzQ1+RIpQlri8qx9Xht0CWosX0TsbAwG+P7J7W74aOc56FTy6FVyPD9yQbIeA6iyMBx0uvKNQczDIBHYIhTSTMUQM//dh+OGjiR+hz8/mQ9jHY3zE4PjA43ZBwHlZyHQauAy8OisqEq6T4hBTOLFy/GokWL4HBISYq7d+/Ge++9h2XLluGNN94Iy8CqqqRvfOnp6X63p6en+44Fs2zZMixdujQsYzgfrdnGHt+2VEGAw9W+LstdSRClmZiLB6Rg85Fq1FtdaLD6Lz/JeQ7ZBjUsLgEeR+ul/L08gogmqwuPfLQPDVYXGGMQm3dPWV0CzjbYkJOk9QtonIIIUWRYseUEakyOoDkPF/oGfqbBDrvbg6wENSoaHXB7RClYaM4DEgRpBmnKoBS/QMQjiPjmRB3WFpXhcKU54PkoZBwKcxMxc0QGpg5OaVcQ491OHa9SQNPcGuJAmREnay1Ii1ej1uxsTnaFb3yA9BrpNYqQllpiVWdr4ETic7BlUJ2hV6PJ5oLTI/XacnhEDM+Mx5LZw2iJvxcLKZi58847odFo8MQTT8Bms+Gmm25CdnY2XnzxxYgs/7S0ZMkSLF682PezyWRCTk5OWO6b1mxjz4gsPZJ1ShytMrc7+bQriQyIU8mwr8wIpYwHD84XWJw7h6Ha4oTb077gizGGP3x6CLVmJzgAchnfPCsizUB4RIYqox15aTrfRdtbAfZsgxVJcSq/GZcl6w7grikD4BFZ0KUqQPoGrtfIYXG44fKISDeo0WBxwSUIEEUpYFDIeWgUMozNTQIgVTr+9EAF1hdXoM4SeKFL0CgQp5LD6fbgeLUZp2ot+OxApa9hZDAapUxaOgtSldd70U2LV0Ip51FrdsLp8Q8OeUjBo93dsaWWWNeZGjjd/TkYLKhOjFPA4RLhFgQYHR4YNEpMHJAclscjsSmkYMZut2P+/Pm4+eabYbPZcPDgQWzfvh19+vQJ28AyMjIAANXV1cjMzPTdXl1djTFjxrT6eyqVqstaKnjrNUhN5PiAJnK94VtdrPn+ZD1qzE4pkIl8LAMASItXwezwIDdJC4vTg7JGm99xxgCHu/2zSC6B4XS9FRy8eSHSjiiFjEl5NpAScq1OD2Q8jyabC3a3CDkHJGikXCIOHNS8DDqViPImO/74yWGo5TwsLk/QpSqRMWjkPHgeMNs9yExQIztRDaebQWAi+Oay+QNSdeB54PlNx/DV0Rq4ggRoI7P0KMxNwFdHqmFzefwaRp6steDvXx7H4isG+wIahYxHvFoOnUoOeRtVeVtedHUqOeJUMjhcIjyiCJdHRKPNBZdHhNnhgUbBItZuINZ09+dgsGUtDhw0Shk0kEEhl+FkLS3v93YhZUTOnTsX//nPfwAALpcL11xzDf7+979j3rx5WLlyZVgG1r9/f2RkZGDLli2+20wmE3bt2hWxAn3eeg06lQxVJifsbgGiyGB3C6gyOXvNt7pY4f1GJ4gMuUlaaJSRTwBO0Slhskv5JwB8264BaTYD6FjMJeelInQeUUoS5lpUrZHxHBQy3ndLrdmFRqsLNpcAd/PW79MNVvxUZ4PF6YHF6UFFk0OqCcMY4jVy8BwHh1tAeaMdFqcHIpMCJLdHhFNg0Knk0ChlqLO44PRIrRpkPA+zQ9rhZLS7cfc7e/D5wSq/QEbOc7hieDr++fNCrLhhDA5VmmF3i75aMHxzPkSKTgmbS8B7u88iTilHVoIGOUlaJGiVbQYywLmLbqNNqmnjvQDGqxVIilNCq5RjVJ8EvHDdGLz2i3F4+5fjKZBph+7+HGzPslZH6+OQniekT/eioiJMmTIFAPDhhx8iPT0dp0+fxn/+8x+89NJL7b4fi8WC4uJiFBcXA5CSfouLi3HmzBlwHIcHH3wQzzzzDDZs2IADBw7glltuQVZWll8tmu7mTaAblhkPm9ODGosTNqcHwzLjaVt2lGn5jS5erUD/lNC6JIeLUsbD6ZaaKZrsbpTWWqULgTd/o4MzR3KeA2Pw5ZSITApCWItwSMZzkMuk3JXB6TqYndIuJgZpi7IgAjaXB+WNNlQZ7RCZ1ATTe/9qBQ9wUp5LtckBl0do3g3EYHa4kZcWj0dnDcGAVB0cLg/qrE40WJywuUU0WN0oqbH4jTlRq8Atk/pizd0TsWT2UAxOj/drGNkyGAMnbUdP1CpR3mhDjdnZofYCF7roxqvleHTmEEwfmoZRfQz0JaQD2vM5GK6Cei1n2IKh5f3IiabisSEtM9lsNsTHxwMANm/ejAULFoDneUycONFXDbg9fvzxR1x66aW+n725LrfeeiveeustPProo7Barbj77rvR1NSESy65BBs3boxYjRmvaGoiR1p3/je6SJeodwkilHIeIgOqzU543y7Btmu3h+93mu/A46uXIi3FyJq3rIrNszYHyo3wNCf6ugXmuw/GpMrEruZj3sJ3CpkMKTqV1JgQDE63ALtbWkIyO9zQKmW+fJbUeDXe3vETtpfWB11KGpimw7WF2Zg+JA1Kuf93KKPDBbfIoG8OojiOg4znmpN1Ocg4DmanJ6Rv3tHWwbonaetzsKMF9dpqUkrL+9Ep2orHcqwjzVGa5efn484778T8+fMxcuRIbNy4EZMmTcKePXswZ86cNncbdTeTyQSDwQCj0Qi9nt7svcmBMiPueedHxKnkvm/0B8qNER7VOW0FMecf41v87K0zo5RzcHmkHTpCkDuSNfdKYEyqyOvty8PzHFweMaCqbksqOYf+yXEQAdhcAurMDjg8DGqFlNQ7ICUON47PgciAtXvLsetkQ8Bz4TngkkEpWFjQByOz9a0Gk8erLHhqwwHEqRTQKmUBu5fsbgE2pwev/WJct3f0Jh3X2nb+xuYE6/NnsNtzUTx3nwIStAqoZDyczd3Wg90n6Vod/TcOVUeu3yHNzDz11FO46aab8NBDD+Hyyy/35bBs3rw5aDE9QiKhrW900aA93yI4AIlxClgcUiVgaVkJSIpTosnuBpq3GvNAQHAiMCmgSdAqpJmX5q3T7cmF9ggMFpcAbXMQ6A2geO9uKIsLz20+jmqTM+B349VyzBmVibljspCub30WleM4xClluGRQMoZk6HGk0ow4pf8yUri+eUdLB+uerqMF9dpbSbg9M2wUsHaPaC0eG1Iwc+211+KSSy5BZWUlRo8e7bv98ssvx/z588M2OEI6w5sz8fi6A6gyOZGgVUR6SO2mlPPQqeTINKjRYHVBUEpdo739dNCcXaLkeXgYg1ImBSne3UteGrkMd17SH69/ewoc15wsLLA2gxk5J+XTNFicYHFKqQ6NCChlHJRyHiaHG032wLYQfZO0mF+YjSuGp0PTRm6LWiGDTi2HrsV26vP/nc7/5k2J9bGhIwX1RmTpO3RRDOeyFgldtBaPDSmYAaSt097t017jx4/v9IAICafzv9HFCgXP4cUbxmByXorvwztBIwVjTXY3TtVZ8cLmY7C5BMh5rjm3BOA5vrkAH4MgMCjlPDINWmgUMjjcIhxu4YKzMgzS7I/TLSX9eqQixHAJDK4grQbG90/CwsJsjOub2Orsl0ImBWc6tRyKILtSKLelZ+hIQb1QLorBZtioX173itbisSEHM4TEipbf6K5+5btID6dd5DIO8WpFq8sjSVqlVKYfQMs5EI47tx+I8dIMTEKcAnlpOvzwU2BeS0syDr5WCr7Zm+Zc3vN/j+OAOKUcShmH2yf3D9oXiec4xKnkiFfL27ULKVKJ9bQ8ET4dKagXjotitC559GTRWjyWghnSK8RazoTR7sED7+3FXVMH4KbxuQEfxCOy9MhNjsP+sqbmgEbKZfHOyogig0ImJeumxKkwdVAKdpTUtfp4HODLpXEHyyZupuClb8x6tQIcB9TbXDA6/C82WqU0AxOnlHU4T6m7/51oeSK8OrLz6FCFqV0XxQaLC9uO1wYNNKN1yaMni9bdZZGvIkYICcAAnG6w4ekNhzD31e98gYi3rsO3JXVYWJgtbbP2MLgFAS5BhNMjwi0wCAxwiyKSdUoMy4jHNyfqoJK3/p+7t/FiazQKGbIMavRL1iKxeVbIJTAoOA4GtdQuIDlOhdwkLTIMUrXgaEu4Pp93eeJIpQlxKjnS4lWIU8l9yxNtBX8kuI4U1Du/qGFL3pYbNreAv248gkfe34d73vkRt67a7ffvQgX1ul+0Fo+lmRlCopRSJiXsHq0yY8na/fj5xL745kSd3yxCVoIGZxpsOL+0Cw9pS3aN2Ym3d/6EY1UmpMSrpP5EbhE8DzARuFAbSw5S/kx2ohp8i4J2UtE8Dwan6zBtSAo0ytj6KKHlia7T3vynYAn63sTvWrMDVqcArVIGnVrRah5MtC559HTRmOMWW59AhPQScl5qCwCIEBlDlcmJ5zYfQ7xKjqQ4FRS8VEiuzuwCBw4yTlpi4jip2JxKLkNSnAJmh4D3fjgDt4dBr+aRrFOhqskOQWy7xk28Wo5UnQpmpxt1FheqTc7m5EoOHlEKZPRqGX5z+aBOBzKRyFmh5Ymu1d78p9YuihzHQauUITdJ22agGe4lD8qfar9oKx5LwQwhUYhrbiEgiAwipMq+AGDjBChkHlicbjg9UrdtgUkzMRkGNZTNfY3kMk6KVtQc6i0ugAOsLqn4nNBKnRmpJo0SiVo5eE6atterFbC5RGTo1TA73LC5pCn94Vn6sHwDi1TOSrTuyOhJ2pv/dP5FscHiwl83HoFOLe3es7sEeEQRcp6HWsEHBJrh2tZP+VMdF025iBTMEBKFWkvCdbhF2N0O8M0tC8ABApMCnhqzA5kGDZQK3hetKHip9owoMjTaArdVeylkQL/kOL/eSBzHQRAZ4lUyPP8/o8FzXFi/gUVySy0tT0SXlhfFbcdr4REBl0dEpdEOp0f0tdhQNedmtcyDCceSB23vjn0UzBASQ1iLv3CQLgLe0r+iCNRbXMhOVIOJgNHhRqPNDU+QvgU8BylJF4DdI0DhvaBz0pZqGcdJu5WsUvPAUdnhbcQY6ZyVaN2REcvCtUSTpFVCZCIqjC6IDM11lKT3vt0tosJoh14t9ws0O7PkEen3IgkPCmYIiRG++jHNf0TGWvaYBscBTo+AKqMTVpcnaO8ljUIGOQ/IZByUPI+c5Dhc1DcRn+6vQL3VjUStAgp511ffjXTOSlvJp1R1uOPCuUQzLCMeApNaaijlnK9XFwdAzjO4PNLS6rCMeL/fC3XJI9LvRRIeFMwQEiO4c5Mwvm7XLYsreFemzE7/VgMcgMkDk7GwsA9GZetRWmOD0eFCcpwKhbkJ0GsUmDo41XcxMjk8Xb4zIRpyVqJxR0YsCvcSzZEqM2TNndMFEQDPpJkZBgjNHeBlHIcjVeawBBfR8F4knUfBDCEx4vz+9iJjEANbJPnwHHDJwBTcPXUAshI0AKQLwbj+idCp/KvydvfOhGjJWYm2HRmxRhQZ/rG1BE02FwwapS+3Ra0IfYmmweYCz3HITtSg3uKC0yOAiefuN1mnhM0lhC24iJb3IukcCmYIiVGtFepNiVPi8mHpuHliDnQqhW+bq04lh7aNqrzduTMhmnJWomlHRqxZvfsMdp9qhMgYLE67L0k3NV4qnBjKEo03uFDKePRL0cLhEs/tZlLycLhFKHgxbMFFNL0XSeioAjAhMeJCDSJlHIdpg1Pw7p0TcM+0AUjWqZGsk6rypupUOFlrxTcn6nCgzAgxWEJNN4rWKqKk/XaU1OHlr0/ALUhFGOW8lN9id4sob7TD4vSEVIG3ZWVgMECjlCFerYBGKQMY0GRzIy9NF7bggt6LPQPNzBDSQwiM4dvjdfifszvxq2l5uHf6QADRUz/j/N0uEwckU85KjPLuAHK6RUgVArjmGQ0GGSfVRaoyOpCVoOrwEk0kkrMpfyr2cez8phg9jMlkgsFggNFohF5P04S9Xb/HPov0ELqEnJdyZBikjtcynsPvZg3BiCxD0OTMRpsLChmPWyb1wyUDU7o8T6StgIpyVmLPgTIj7nnnR2hVMlQZnXC4BfAcB48o1YTxXlTkPDAsU4/1iy7p8L+p33umObjo6iCcKgBHl45cv2lmhpAYJIN/XyUZz/u2sPKcCJeH4dX/K8WIrPiA+hkeN4PdJaDB7cLfvzyG/+w4hYHpHf8G2t4PfipI1vN4dwCpZDKkxqtwtsEGlyBVqW75DhBEqT/Y9yfrO/xvHInkbMqfil0UzBASg85vEMkYfFcRnuMhl4kwOdw4VGFCavy5QMbi9KC80Q6RSbM3YIBcxnc4sGjv0hUVJOuZWu4AilPJoJBJ1aKBc7MyHKQWG26BhfxvHAvBBc3mRAcKZgiJcRykbast8RzgYYDLc65+BgNDrdkJkTFf7yZPc1CToVe1O7DoyExLrBYkowtU21ruADKo5fCIDEq59D4TRQaRMWkbdZwSDo8Ylf/G4RAt+WiEghlCYh7HBQYzYnO9D6X8XP0Mh0uE0yNAxnPgwEGEVIxMzvPtDiw6OtMSiwXJ6AJ1YS2TdOssTogig1SihYMIadkzrfn9EY3/xuFAy6fRhbZmExJjOEgzL14yjvNrECkyER6BQa9WYESWHo02NxhjvuRMqZoqg0dkUMmlTsQA2rWNtiMzLYD/ckQw0VaQzHuBOlJpQpxKjrR4FeJUct8FakdJXau/K4oMB8qM2Ha8Niq2v3c17w6g/qk6AIBHlAo5ahQ8shM10Kmk78rR9m8cDucH9WqFDDzPNRcLVMHiFLByW2mPfw9EE5qZISTGeLsYeAMaj8gAToS356R3N9OiS/N8u5mqTE5oFDwABkGUzpNxnF8+TXsuOh2daYmlgmSdye/prbM5kwem4MN+Sbj2tZ04WWtFqk4JTYvCjNH2bxwusbp82pPRzAwhMUhgQLZBhVsn9YVeo4AgNjfgExkMGgV+N2sI7pqa5/v2PCwzHoLIwHFSoqZa7v/t2XvRuVAxso7OtMRSQbKOzjp5tWc2pyfP2sjlPB6dOQRJcQoYHR44PGLU/huHS3uC+o4WCySdQzMzhMSos01OvLPzNHgOUMh48ABUCh4jsuIxIuvct8GWW1y/K6nFf3aehssjQsZzEEXWoWJkocy0xEpBslDye9ozm7PsiyMwaBQ4WWvtsbM2sfJvHC7Uzyn6UDBDSAzzMEjrTqIIDoBCzuFghQmLP9iHWyf1xSUDU307cUb1MWBElh7xagXe230GNSYnAEAha/9FJ9TqrLHQ0LGjFyhRZFhfXIHDFSZolTL/AiuQZnNUch6HK82IV8mQGq/u0UmisfBvHC6xtHzaW1AwQ3qNnjS1HwwDYHJIFWiMdg/+tvk43tl52jcLAMD3zdnlEQEOyNCrccP4XNw0PrfdF51Qv4VHe82QjlygvDkyh8qNaLS7YbQDjTY3UuNV55buwNBkc0FkDAaN0telvCfX2In2f+NwiUTLBdI2amdAeo0DZUZc/cp3kR5Gt0qNV4IxDt6VE0Fk57U1kD54Q5kh6Im1WM5ttxWCXqD+Mn8UAPi25GoUMlQZHQAnFS7kOc6Xi2R3Cfip3gKAQ7/kOKgVUsdnbwdoBqkS82u/GNcrAoCeKBItF3oTamdASBC9MRnP7PAgLyUOx2ssAIDBaTrwvBTZdHaGoCd+C7/QrNPEAcm4ddVuX44MADTaXLC7Rch5KVisNTsRp5LBLQgQRECr5OERRfxUb4fTc257vFLGQy7je+X7sqfoTUtr0Y6CGRLTOjI70BuT8TwCg9HhAWvud+D0MGhavAyd3Ubqff3rrE40Wd1I1CqQrFPF9Ad6WxeoA2XGgB1PqfFqlDfa4REZeA5wuD0w2twwOz3geQ4aBY+KJgcExiDnOanODwCHWwA8Is422CL7hEmn9MSgPhZFfTDzhz/8AUuXLvW7bciQITh69GiERkSiRbDaHgNS4zBrZCZykrQBwc2wjPgIj7j7CYzB4vRAFAGeBzyiCKlN5TmhVmj1vv6HK4wwOTxSFVieg16twPAsfUxPtbd2gQq240mnkiM7UYNaswNOtwiRATaXgJFZBjTZXDhWbYbIGBR8yzwcaXWf54CNB6s6lLNECAkU9cEMAIwYMQJfffWV72e5PCaGTbpQsFLiTXYXdp1qwM6TDdCp5IhTyvzWrw9UGCM97G7HGGCyuyECYCLz1YdhjPnyNzwig5yD3y6dC8127Sipw5K1+1FnccLhYQCTAhlRZDDa3dhf1nTB3TqxmHPT2o4n6f0Whya7GzanB0/MGY65Y7KwevcZPL3hEMC84QsDY9JylIznkRqvwslaKq5GSGfFRFQgl8uRkZER6WGQKBGstofF6UGtWdo5AgZ4BBFaldJvC+y7u85EeugR4d3ExQDUmhwQRcDidPvyN0TGoNcoYLS7gs52penVmDkiA5cMTPFtNV32xRGUN9khiOe6JDORQc7zEBmDR5BmhHpaxdy2djwBgMMtYkS2AXPHZIHnOeQkaaFTyeERRLgEEUyU8mXUChlS41XQKmSosTgpb4aQToqJYObEiRPIysqCWq3GpEmTsGzZMuTm5gY91+l0wul0+n42mUxBzyOx6/xKrYwx1JqlnAQFz4MB0gwEO9cN+h9bS1FSbY700CPOLQJVJoevv5M3GdXtEfHAmmLIeSn4SdQq4RJE1JicqDI5sL+sCW98q8DwLAMGpulwuNIM1iKQ4SD9nlsQIW+euUhSKIPm4sRyg76ObslN0ioRp5RBq1ICjPPtZFIreXDgYHcLVFyNkDCI+nYGEyZMwFtvvYWNGzdi5cqVOHXqFKZMmQKzOfiFadmyZTAYDL4/OTk53Txi0tXOz1twuEU4PWJzcmVzgiWT8kO8Ca7HqsywuIQIjzx6MEgtEcTm/7e7BDRYXag1uxCnlMEjMlQ2OaTgpDlp1e4ScbjCiHe/P928TNLiDjkpoGGQllB8QdIFKubGYoO+li0ibE4PaixO2JweDMuMDwjEvDM5TTYP1Aoe8WqF1LsIXLtbSBBCLizqZ2Zmz57t+3t+fj4mTJiAvn374v3338cdd9wRcP6SJUuwePFi388mk4kCmh7m/LyFlt2ggXOzDXL+XDdop0eAw+2J4Kijm7fTEgNQ1miHQi4tF8llUkdujjG4RRFJCgUarG7fud4ABpB+4Jg0Q+Od9Tl/1qGnNOhr75ZcKq5GSPeI+mDmfAkJCRg8eDBKSkqCHlepVFCpVN08KtKdzs9bkPO8b7ur1BWaQa2QQa3k4RZEVJscMDs8iN7v+tFFYIDgFsEDEKVG2wAYRJHBLbAWMzDnfqdlMAkAMh6wuwUMy9T7zTqE0v8oWrV3S25v61tESCTEXDBjsVhQWlqKX/ziF5EeComQ87/tGjRyKGW8VLcDgIznEa+W42yDHUa7O8KjjV0iAJev+ZM0C2N3C36zOC21rCWukPHQqeQBsw69tUEfFVcjpGtFfc7MI488gm3btuGnn37Cjh07MH/+fMhkMtx4442RHhqJoJZ5C3aXALmMP7erBgyVRkdAIEOXjc6Rej+dW6qT88FfU4WMw5icxKCJvN5ZtUabG+d3UunpOSTemZxpg1Mxqo+BAhlCwijqZ2bKyspw4403or6+Hqmpqbjkkkvw/fffIzU1NdJDIxHm/ba7o7Qea/eW4cvD1TA7PBAE/4tkik6Jq/Oz8NmBCtSYo3/5IlYwcFDKOd9WbJ4DkuOUeGDG4FaLwFEOCSGkK1CjSRKzDleYsGr7KawvrvAVg2tpeKYeiy7Nw8wRGdheWo8H1+xFo42WncIhOU4Jp0eE0yM0Ly9JReCe/Nlw/Hxi3wv+PjXoI4RcCDWaJD2WIDJ8daQab357Crt/agg4rpBxuGZ0Fn55cX+MzD6XnJmkVcJOW7M7TapPwyFeLUemWg6HS6oizHMczA4PcpK07bofyiEhhIQTBTMkJhjtbqzZfQb/2Xka5U32gOMpOiV+PqEvbp7YF6nxgbvZhqTpgs7ekPbhAXA8B2+sIeelom8apQyATCr+JutY4i416COEhAsFMySqldSY8ca30lKS3R04szI8U487L+mPn43OglLeej77Zwer0LMXVLuWRskD4GBzCdAqZVArzr3W3sTdYZnxPTJxlxAS/SiYIVGHMYYvD1fjrR0/YUdpfcBxngMuG5qGu6YOwPh+SQHF14Ipb7KdV+GNtJeMAxK0Klicbsh4DjKeh8MjUuIuISRqUDBDokajzYn//lCG93afwel6W8DxeLUcCwqzccfF/ZGbHNeh+85O0IIHQFkz7ccBkPMctCo5GGPI75OAqYNS8M2JOir+RgiJKhTMkIgSRIbjVWb85/uf8Mm+SlicgS0H+iZrcdP4XNw4Phd6jSKkx7k6PxNLPzmEpl5WRM87T9LahJRBI4dBo4DIALPdDZmMw+QBKRjXNxGjcxPAcxya7G6/BN07LhlAibuEkKhCwQzpdowxWJwebC+pw+pdZ/BdSR2C9RWcOCAJN0/IxZXDM6BSBFaL7Qi5nMf8wmys2v5Tp+4nlvAckKCRIzdZhyuHpyEtXo3jVWbYPCJ0KhkOlBlxqs4Ku1uqupufk9CuGRZK3CWERBsKZki3cbgF1Ftc+GRfBT4sKkNJjSXgHI1ChlkjM3Dj+FyMzjFAJe9cENPS9CFpPSqYSdQqcN+0AfjvD2X4qcHq65WklPPISdTg2rE5uGRgSqszJ6LIaIaFENIjUDBDupRbEGFxeHC63oq1ReX4ZH9F0MJ1mQY15hdkY35BNnKStFB3ciYmmGjs98OjucO3jG/OT5FhVLYBo7INMKgV+KnBhq+PVqOyyQGBNeexyDgMSdfh8auGY/LAFNw5JQ8Hyo3Ye7YJHAPG5CZgVPaFy+XTDAshpKegYIaEnSgyWFweWBwe7C9rwod7yrD1WC08QdaSCnITsKAgG9OGpCFVp2quW9I1omXbsErBI14lx+0X98fFzUs65+eltPSHq0e0GazwPIfROQkYnZPQ3U+FEEKiAgUzJGzsLgFmhxsmhxvfHK/D2qIyHKwwBZynlPOYMSwNCwqyMTRTj6Q4JbTKrn8rfn8ycJt3d+E5QMZz0KsVGJ6l79DuHwpWCCGkbRTMkE5xeURYnNIsTIPVic8OVGJ9cQVqzM6Ac1N0Sswbk405ozKREq9CYpwSOlX3vAVFkWHlttJueSwvngN+NioT8wqyYbR7kKhVIFmnotwUQggJMwpmSIcJorQbyeL0wOkWcKrOinV7y/Hl4Wo4PcEbPi4szMaUQSnQKOVI0CoQrw5ti3WoDlWYUBok4bgrqOQchmbo8duZQ3DJIOruTgghXY2CGdIujDHYXAIsTg9sLgGCKGLXyQasLSrDnjNNAefLeQ7Th6RiQWE2hmboIed5JMQpEK+St6tib7g12FxwBQm0wknBAw/OGIypg9No9oUQQroRBTOkTQ63FMBYnR4IIoPV6cGmQ1VYt7ciaMPHBI0CV4/OxDWjs5CsU0HGc0jQKKHXRCaI8UrSKs9VkOsCGXoV/n7dGKqCSwghEUDBDAngEaQ8GLPDA3dz8ZLyJjvW7S3HxoNVsLkCmwIMTNVhQWE2LhuaBqWcB89xSNAqoFcromKGYkSWHhl6NeosrrDdp5wHUuNVuHdaHn4xsV9UPE9CCOmNKJghAKQEWatLyoOxNwcrjDHsPdOEj4rK8f3J+oCS+DwHTM5LwcKx2cjPNoDjOPAcB71GgQRNdAQxXjzP4YbxuXji44Oduh+tgsO7d02CyeGhQnOEEBIlKJjp5ewuAWanGzanAJFJ4YrTLeDLIzVYt7ccp+qsAb8Tp5LhqpGZmF+QjQyDGgDAcRz0ajkStErIovTiflMngxkOwENXDEFBbmL4BkUIIaTTKJjphVpup/aI55Jia81OfFxcjs/2V8LkCGz4mJOowYLCbFw5PMNX3I7jOOhUciRqFZDL+G57DqHozAyKWsHj4SsG466peWEcESGEkHCgYKaXOH87tRdjDIcrTVhbVI5tx2uDNnwc3y8RCwr7YFy/RPAtknh1ajkStUooojyICdWVw1Ihk8lw2dA0zB+TDbm8Zz5PQgiJdRTM9GCMMdjdAswOaTs1Y+ciFbcgYtvxWnxUVI5jVeaA31XLecwckYH5BdnITdb6HdOppOUkZQ+/uP/r1vGRHgIhhJB2oGCmB3J6pADGu526pUabC5/uq8SGfRWotwbu7EnXqzBvTDauGpURUNhOq5QjMU4R1k7WhBBCSGdRMNNDeAQRVqeUzBusOFxJjQUfFZXh66M1cAuBa0n5fQxYUJiNi/NSAhJ4NUoZErXKLulkTQghhHQWBTMxjDEGq0uAxeGBzRWYsCuIDDtK6/FRURn2lxkDjitkHC4bKjV8HJQeH3BcpZAhSavs0k7WhBBCSGdRMBODHO5zy0giC5xlsTg8+PxgJT7eW4EqkyPgeFKcEnNHZ+FnozORqFUGHFfK+W7rZE0IIYR0Fl2tYoRbEGFxSLuRvFV5z3emwYZ1ReXYdLgKDnfgOUPS47GgMBvTh6QG3YGkkPHd2smaEEIICQe6akUxUWSwuKR6MA53YAsBABAZw48/NWJtURl2/9QYcJzngKmDpIaPI7L0QfsjKWR8RDpZE0IIIeFAwUwUsjUHMNbztlO3ZHcJ2HxYavh4psEWcFyvlmNOfibmjs5Cml4d9D4i3cmaEEIICQcKZqKE0yMl8lqdgl9V3vNVmRz4eG85Pj9QBYszMOm3X7IWCwr7YMawtFZ3H0VLJ2tCCCEkHCiYiSBBZLA4PK1up/ZijGF/uRFri8qxvaQuoEovB2DigGQsLMxGQW5CqwFKtHWyJoQQQsKBgplu1nI7td3d+jISIPVQ+vpoDdYWlaOk1hJwXKuUYdbIDMwfk43sRE2r9+PtZG3QKKK2CWR36RPPo8zceuDY8jxCCCGxISaCmVdffRXPPfccqqqqMHr0aLz88ssYPz62Ss1faDt1S/UWJzbsq8An+yrRZHcHHM9KUGNBQTZmjshAXBs7j2Khk3V3+9t1F+H6N3e16zxCCCGxIeqDmf/+979YvHgx/vnPf2LChAlYsWIFZs6ciWPHjiEtLS3Sw2uTWxBhdXpgdrS+nbqlo1VSw8etx2rhCdLxsTA3AQsL+2DCgCS/ho/ni6VO1t3torxkaBQ87EG2rntpFDwuykvuxlERQgjpDI61tc4RBSZMmICLLroIr7zyCgBAFEXk5OTg/vvvx2OPPXbB3zeZTDAYDDAajdDr9V09XIgig9UlBTCtbaduySOI+K6kDh8VleNQhSnguFLO44ph6VhQmI3+KXEXvL+e3sk6HHaU1OHWf++GO0jAqOA5vH37eEwemBKBkRFCCPHqyPU7qmdmXC4X9uzZgyVLlvhu43keM2bMwM6dO4P+jtPphNPp9P1sMgUGCF3B7pL6IlmdbefBeBntbny2vxLriytQa3EGHE/RKTFvTDbmjMqEQXvh+i9xKimI6emdrMNh8sAUvH37eLy85Tj2nG2CR2CQyziMzUnA/ZcPpkCGEEJiTFQHM3V1dRAEAenp6X63p6en4+jRo0F/Z9myZVi6dGl3DA8eQYTJIdWEaWs7dUun6qxYW1SOr45UwxlkB9PwTD0WFmZjyqCUdi0RUSfr0EwemIKJA5JxqMKEBpsLSVolRmTpaZcXIYTEoKgOZkKxZMkSLF682PezyWRCTk5OlzyW1SWgyea64HkiY/j+ZD3WFpWj6ExTwHE5z2H6EKlK79CM9i2FUSfrzuN5DqP6GCI9DEIIIZ0U1cFMSkoKZDIZqqur/W6vrq5GRkZG0N9RqVRQqVTdMbwLsjo92HioCuv2lqOiKbDhY4JGgatHZ+Ka0VlI1rVvzNTJmhBCCPEX1cGMUqnE2LFjsWXLFsybNw+AlAC8ZcsW/PrXv47s4NpQ3mjHur3l2HioCjZXYBLwwFQdFo7NxqVD0tqd40KdrAkhhJDgov7KuHjxYtx6660YN24cxo8fjxUrVsBqteKXv/xlpIfmhzGGvWea8GFRGXadbMD5KcA8B1w8MAULCrORn21odxsB6mRNCCGEtC3qr5DXX389amtr8dRTT6GqqgpjxozBxo0bA5KCI8XhFvDVkRqs21uOU3XWgOM6lRxXjcrAvDHZyDAEb/gYDHWyJoQQQton6uvMdFZX1ZmpNNrx+jen8OGeszA5Ahs+5iZpMb8gG1eOSIemA0m6cp6HQauAXk1NIAkhhPRePabOTLTaXlKHW/69G0KQomvj+ydhYWE2xvZNbLNK7/mokzUhhBASGgpmQlCYm4h4tRxNNqlvklrBY+aIDMwvyEZukrZD98VzHAzNTSCpxgkhhBDScRTMhECjlOHG8blYX1yOa0Zn4aqRmdCpO/ZSUidrQgghJDwoZyZENpcHDrfYrqJ5LVEna0IIIeTCKGemG2iVcrgFd7vPp07WhBBCSNegYKYbUCdrQgghpOtQMNOFqJM1IYQQ0vUomOkC1MmaEEII6T4UzISRWiFDUhx1siaEEEK6EwUzYUCdrAkhhJDIoWCmE5QyHul6NeKoCSQhhBASMXQV7gSaiSGEEEIij7bZEEIIISSmUTBDCCGEkJhGwQwhhBBCYhoFM4QQQgiJaRTMEEIIISSmUTBDCCGEkJhGwQwhhBBCYhoFM4QQQgiJaRTMEEIIISSmUTBDCCGEkJhGwQwhhBBCYhoFM4QQQgiJaRTMEEIIISSmUTBDCCGEkJhGwQwhhBBCYpo80gPoaowxAIDJZIrwSAghhBDSXt7rtvc63pYeH8yYzWYAQE5OToRHQgghhJCOMpvNMBgMbZ7DsfaEPDFMFEVUVFQgPj4eHMdFejgRZzKZkJOTg7Nnz0Kv10d6OBFHr4c/ej0C0Wvij14Pf/R6+Avn68EYg9lsRlZWFni+7ayYHj8zw/M8+vTpE+lhRB29Xk//4bVAr4c/ej0C0Wvij14Pf/R6+AvX63GhGRkvSgAmhBBCSEyjYIYQQgghMY2CmV5GpVLh6aefhkqlivRQogK9Hv7o9QhEr4k/ej380evhL1KvR49PACaEEEJIz0YzM4QQQgiJaRTMEEIIISSmUTBDCCGEkJhGwQwhhBBCYhoFMz1MQ0MDbr75Zuj1eiQkJOCOO+6AxWJp8/z7778fQ4YMgUajQW5uLh544AEYjUa/8ziOC/izZs2arn46IXn11VfRr18/qNVqTJgwAbt3727z/A8++ABDhw6FWq3GqFGj8Pnnn/sdZ4zhqaeeQmZmJjQaDWbMmIETJ0505VMIq468Hq+//jqmTJmCxMREJCYmYsaMGQHn33bbbQHvhVmzZnX10wibjrweb731VsBzVavVfuf0pvfH9OnTg34WzJkzx3dOLL8/vvnmG1x99dXIysoCx3H4+OOPL/g7W7duRWFhIVQqFQYOHIi33nor4JyOfiZFk46+JmvXrsUVV1yB1NRU6PV6TJo0CZs2bfI75w9/+EPAe2To0KGdGygjPcqsWbPY6NGj2ffff8++/fZbNnDgQHbjjTe2ev6BAwfYggUL2IYNG1hJSQnbsmULGzRoEFu4cKHfeQDYqlWrWGVlpe+P3W7v6qfTYWvWrGFKpZL9+9//ZocOHWJ33XUXS0hIYNXV1UHP3759O5PJZOyvf/0rO3z4MHviiSeYQqFgBw4c8J2zfPlyZjAY2Mcff8z27dvHrrnmGta/f/+ofP7n6+jrcdNNN7FXX32V7d27lx05coTddtttzGAwsLKyMt85t956K5s1a5bfe6GhoaG7nlKndPT1WLVqFdPr9X7Ptaqqyu+c3vT+qK+v93stDh48yGQyGVu1apXvnFh+f3z++efs97//PVu7di0DwNatW9fm+SdPnmRarZYtXryYHT58mL388stMJpOxjRs3+s7p6GscbTr6mvzmN79hzz77LNu9ezc7fvw4W7JkCVMoFKyoqMh3ztNPP81GjBjh9x6pra3t1DgpmOlBDh8+zACwH374wXfbF198wTiOY+Xl5e2+n/fff58plUrmdrt9t7XnTRwNxo8fzxYtWuT7WRAElpWVxZYtWxb0/Ouuu47NmTPH77YJEyawe+65hzHGmCiKLCMjgz333HO+401NTUylUrH33nuvC55BeHX09Tifx+Nh8fHx7O233/bdduutt7K5c+eGe6jdoqOvx6pVq5jBYGj1/nr7++OFF15g8fHxzGKx+G6L5fdHS+35zHv00UfZiBEj/G67/vrr2cyZM30/d/Y1jiahXgeGDx/Oli5d6vv56aefZqNHjw7fwBhjtMzUg+zcuRMJCQkYN26c77YZM2aA53ns2rWr3fdjNBqh1+shl/u37lq0aBFSUlIwfvx4/Pvf/25XW/bu5HK5sGfPHsyYMcN3G8/zmDFjBnbu3Bn0d3bu3Ol3PgDMnDnTd/6pU6dQVVXld47BYMCECRNavc9oEcrrcT6bzQa3242kpCS/27du3Yq0tDQMGTIE9913H+rr68M69q4Q6uthsVjQt29f5OTkYO7cuTh06JDvWG9/f7z55pu44YYbEBcX53d7LL4/QnGhz49wvMaxThRFmM3mgM+QEydOICsrCwMGDMDNN9+MM2fOdOpxKJjpQaqqqpCWluZ3m1wuR1JSEqqqqtp1H3V1dfjTn/6Eu+++2+/2P/7xj3j//ffx5ZdfYuHChfjVr36Fl19+OWxjD4e6ujoIgoD09HS/29PT01t9/lVVVW2e7/3/jtxntAjl9Tjf7373O2RlZfl9GM+aNQv/+c9/sGXLFjz77LPYtm0bZs+eDUEQwjr+cAvl9RgyZAj+/e9/Y/369fjf//1fiKKIyZMno6ysDEDvfn/s3r0bBw8exJ133ul3e6y+P0LR2ueHyWSC3W4Py3+Dse7555+HxWLBdddd57ttwoQJeOutt7Bx40asXLkSp06dwpQpU2A2m0N+nB7fNbsneOyxx/Dss8+2ec6RI0c6/Tgmkwlz5szB8OHD8Yc//MHv2JNPPun7e0FBAaxWK5577jk88MADnX5cEp2WL1+ONWvWYOvWrX5JrzfccIPv76NGjUJ+fj7y8vKwdetWXH755ZEYapeZNGkSJk2a5Pt58uTJGDZsGF577TX86U9/iuDIIu/NN9/EqFGjMH78eL/be9P7g7Rt9erVWLp0KdavX+/3RXv27Nm+v+fn52PChAno27cv3n//fdxxxx0hPRbNzMSAhx9+GEeOHGnzz4ABA5CRkYGamhq/3/V4PGhoaEBGRkabj2E2mzFr1izEx8dj3bp1UCgUbZ4/YcIElJWVwel0dvr5hUtKSgpkMhmqq6v9bq+urm71+WdkZLR5vvf/O3Kf0SKU18Pr+eefx/Lly7F582bk5+e3ee6AAQOQkpKCkpKSTo+5K3Xm9fBSKBQoKCjwPdfe+v6wWq1Ys2ZNuy48sfL+CEVrnx96vR4ajSYs77lYtWbNGtx55514//33A5bizpeQkIDBgwd36j1CwUwMSE1NxdChQ9v8o1QqMWnSJDQ1NWHPnj2+3/36668hiiImTJjQ6v2bTCZceeWVUCqV2LBhQ8DW02CKi4uRmJgYVc3VlEolxo4diy1btvhuE0URW7Zs8ft23dKkSZP8zgeAL7/80nd+//79kZGR4XeOyWTCrl27Wr3PaBHK6wEAf/3rX/GnP/0JGzdu9Mu/ak1ZWRnq6+uRmZkZlnF3lVBfj5YEQcCBAwd8z7U3vj8AqZyB0+nEz3/+8ws+Tqy8P0Jxoc+PcLznYtF7772HX/7yl3jvvff8tu23xmKxoLS0tHPvkbCmE5OImzVrFisoKGC7du1i3333HRs0aJDf1uyysjI2ZMgQtmvXLsYYY0ajkU2YMIGNGjWKlZSU+G2V83g8jDHGNmzYwF5//XV24MABduLECfaPf/yDabVa9tRTT0XkObZlzZo1TKVSsbfeeosdPnyY3X333SwhIcG3nfYXv/gFe+yxx3znb9++ncnlcvb888+zI0eOsKeffjro1uyEhAS2fv16tn//fjZ37tyY2nrbkddj+fLlTKlUsg8//NDvvWA2mxljjJnNZvbII4+wnTt3slOnTrGvvvqKFRYWskGDBjGHwxGR59gRHX09li5dyjZt2sRKS0vZnj172A033MDUajU7dOiQ75ze9P7wuuSSS9j1118fcHusvz/MZjPbu3cv27t3LwPA/v73v7O9e/ey06dPM8YYe+yxx9gvfvEL3/nerdm//e1v2ZEjR9irr74adGt2W69xtOvoa/Luu+8yuVzOXn31Vb/PkKamJt85Dz/8MNu6dSs7deoU2759O5sxYwZLSUlhNTU1IY+Tgpkepr6+nt14441Mp9MxvV7PfvnLX/ouRIwxdurUKQaA/d///R9jjLH/+7//YwCC/jl16hRjTNrePWbMGKbT6VhcXBwbPXo0++c//8kEQYjAM7ywl19+meXm5jKlUsnGjx/Pvv/+e9+xadOmsVtvvdXv/Pfff58NHjyYKZVKNmLECPbZZ5/5HRdFkT355JMsPT2dqVQqdvnll7Njx451x1MJi468Hn379g36Xnj66acZY4zZbDZ25ZVXstTUVKZQKFjfvn3ZXXfdFTMfzIx17PV48MEHfeemp6ezq666yq9eBmO96/3BGGNHjx5lANjmzZsD7ivW3x+tfR56X4Nbb72VTZs2LeB3xowZw5RKJRswYIBfzR2vtl7jaNfR12TatGltns+YtH09MzOTKZVKlp2dza6//npWUlLSqXFyjEXZ/lpCCCGEkA6gnBlCCCGExDQKZgghhBAS0yiYIYQQQkhMo2CGEEIIITGNghlCCCGExDQKZgghhBAS0yiYIYQQQkhMo2CGEEIIIR32zTff4Oqrr0ZWVhY4jsPHH3/c4ftgjOH555/H4MGDoVKpkJ2djT//+c8dvh8KZgghPdr27dsxatQoKBQKzJs3D1u3bgXHcWhqaor00Hz69euHFStWRHoYhHSI1WrF6NGj8eqrr4Z8H7/5zW/wxhtv4Pnnn8fRo0exYcOGgE7s7SEPeQSEEBIDFi9ejDFjxuCLL76ATqeDVqtFZWUlDAZDpIdGSEybPXs2Zs+e3epxp9OJ3//+93jvvffQ1NSEkSNH4tlnn8X06dMBAEeOHMHKlStx8OBBDBkyBIDUvDUUNDNDCOnRSktLcdlll6FPnz5ISEiAUqlERkYGOI4Ler4gCBBFsZtHSUjP8+tf/xo7d+7EmjVrsH//fvzP//wPZs2ahRMnTgAAPvnkEwwYMACffvop+vfvj379+uHOO+9EQ0NDhx+LghlCepnp06fjgQcewKOPPoqkpCRkZGTgD3/4g+94U1MT7rzzTqSmpkKv1+Oyyy7Dvn37AABGoxEymQw//vgjAEAURSQlJWHixIm+3//f//1f5OTktGssZWVluPHGG5GUlIS4uDiMGzcOu3bt8h1fuXIl8vLyoFQqMWTIELzzzjt+v89xHN544w3Mnz8fWq0WgwYNwoYNGwAAP/30EziOQ319PW6//XZwHIe33norYJnprbfeQkJCAjZs2IDhw4dDpVLhzJkz6NevH5555hnccsst0Ol06Nu3LzZs2IDa2lrMnTsXOp0O+fn5vtfC67vvvsOUKVOg0WiQk5ODBx54AFar1Xe8pqYGV199NTQaDfr374933323Xa8VIbHkzJkzWLVqFT744ANMmTIFeXl5eOSRR3DJJZdg1apVAICTJ0/i9OnT+OCDD/Cf//wHb731Fvbs2YNrr7224w/YqTaVhJCYM23aNKbX69kf/vAHdvz4cfb2228zjuN8XZBnzJjBrr76avbDDz+w48ePs4cffpglJyez+vp6xhhjhYWF7LnnnmOMMVZcXMySkpKYUqn0dWe/88472c0333zBcZjNZjZgwAA2ZcoU9u2337ITJ06w//73v2zHjh2MMcbWrl3LFAoFe/XVV9mxY8fY3/72NyaTydjXX3/tuw8ArE+fPmz16tXsxIkT7IEHHmA6nY7V19czj8fDKisrmV6vZytWrGCVlZXMZrP5ugA3NjYyxhhbtWoVUygUbPLkyWz79u3s6NGjzGq1sr59+7KkpCT2z3/+kx0/fpzdd999TK/Xs1mzZrH333+fHTt2jM2bN48NGzaMiaLIGGOspKSExcXFsRdeeIEdP36cbd++nRUUFLDbbrvNN+bZs2ez0aNHs507d7Iff/yRTZ48mWk0GvbCCy907h+WkAgCwNatW+f7+dNPP2UAWFxcnN8fuVzOrrvuOsYYY3fddRcD4Ndlfs+ePQwAO3r0aMcePyzPghASM6ZNm8YuueQSv9suuugi9rvf/Y59++23TK/XM4fD4Xc8Ly+Pvfbaa4wxxhYvXszmzJnDGGNsxYoV7Prrr2ejR49mX3zxBWOMsYEDB7J//etfFxzHa6+9xuLj431B0vkmT57M7rrrLr/b/ud//oddddVVvp8BsCeeeML3s8ViYQB8Y2GMMYPBwFatWuX7OVgwA4AVFxf7PVbfvn3Zz3/+c9/PlZWVDAB78sknfbft3LmTAWCVlZWMMcbuuOMOdvfdd/vdz7fffst4nmd2u50dO3aMAWC7d+/2HT9y5AgDQMEMiWnnBzNr1qxhMpmMHT16lJ04ccLvj/e/l6eeeorJ5XK/+7HZbAyA78tVe1ECMCG9UH5+vt/PmZmZqKmpwb59+2CxWJCcnOx33G63o7S0FAAwbdo0vPnmmxAEAdu2bcOVV16JjIwMbN26Ffn5+SgpKfEl+LWluLgYBQUFSEpKCnr8yJEjuPvuu/1uu/jii/Hiiy+2+lzi4uKg1+tRU1NzwcdvSalUBrwm5993eno6AGDUqFEBt9XU1CAjIwP79u3D/v37/ZaOGGMQRRGnTp3C8ePHIZfLMXbsWN/xoUOHIiEhoUPjJSTaFRQUQBAE1NTUYMqUKUHPufjii+HxeFBaWoq8vDwAwPHjxwEAffv27dDjUTBDSC+kUCj8fuY4DqIowmKxIDMzE1u3bg34He8Fd+rUqTCbzSgqKsI333yDv/zlL8jIyMDy5csxevRoZGVlYdCgQRccg0ajCcdTafW5dIRGowmaENzyvr3Hg93mfTyLxYJ77rkHDzzwQMB95ebm+j6oCekJLBYLSkpKfD+fOnUKxcXFSEpKwuDBg3HzzTfjlltuwd/+9jcUFBSgtrYWW7ZsQX5+PubMmYMZM2agsLAQt99+O1asWAFRFLFo0SJcccUVGDx4cIfGQgnAhBCfwsJCVFVVQS6XY+DAgX5/UlJSAEhBTX5+Pl555RUoFAoMHToUU6dOxd69e/Hpp59i2rRp7Xqs/Px8FBcXt7pzYdiwYdi+fbvfbdu3b8fw4cM79yS7UGFhIQ4fPhzw2g0cOBBKpRJDhw6Fx+PBnj17fL9z7NixqKp5Q0h7/fjjjygoKEBBQQEAqQxCQUEBnnrqKQDAqlWrcMstt+Dhhx/GkCFDMG/ePPzwww/Izc0FAPA8j08++QQpKSmYOnUq5syZg2HDhmHNmjUdHgvNzBBCfGbMmIFJkyZh3rx5+Otf/4rBgwejoqICn332GebPn49x48YBkHZEvfzyy75dB0lJSRg2bBj++9//truA1o033oi//OUvmDdvHpYtW4bMzEzs3bsXWVlZmDRpEn7729/iuuuuQ0FBAWbMmIFPPvkEa9euxVdffdVlz7+zfve732HixIn49a9/jTvvvBNxcXE4fPgwvvzyS7zyyisYMmQIZs2ahXvuuQcrV66EXC7Hgw8+GLZZKkK60/Tp0yGlywSnUCiwdOlSLF26tNVzsrKy8NFHH3V6LDQzQwjx4TgOn3/+OaZOnYpf/vKXGDx4MG644QacPn3alx8CSHkzgiD45cZMnz494La2KJVKbN68GWlpabjqqqswatQoLF++HDKZDAAwb948vPjii3j++ecxYsQIvPbaa1i1alW77z8S8vPzsW3bNhw/fhxTpkzxfUvNysrynbNq1SpkZWVh2rRpWLBgAe6++26kpaVFcNSExD6OtRVWEUIIIYREOZqZIYQQQkhMo2CGENIl/vKXv0Cn0wX901Y/F0II6ShaZiKEdImGhoZWdyppNBpkZ2d384gIIT0VBTOEEEIIiWm0zEQIIYSQmEbBDCGEEEJiGgUzhBBCCIlpFMwQQgghJKZRMEMIIYSQmEbBDCGEEEJiGgUzhBBCCIlp/w8OnJknZSEmoQAAAABJRU5ErkJggg==",
+            "image/png": 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JKk9CBjJAhDMzJ06cwAMPPIBHH3005P4nnngCP//8Mz777DM8/vjj+NOf/oRp06Y165yqquK+++7DeeedhyFDhgTvv+6669CjRw/k5uZi27ZteOihh7B3794Gd0zNnz8f8+bNi+TPok5gy5FK+FQV4fLYBAAhAJ+qYsuRSgzLT23v4RERtRun14+Kai+8/sQMYOqSRAR7rKxWKzZt2lSvcF5RURFGjhwJm82GPXv24JxzzkFVVVWzznnXXXfh008/xTfffINu3bo1eNyXX36Jiy66CEVFRejdu3e9xz0eDzweT/C23W5Hfn4+bDYbLBZLM/9C6qgWf3sI8z7c1eRxj18+CDef17MdRkRE1L5cXgUVTi88PiUq50sz6ZGWHP2qeXa7HVartVnX74hmZoxGI9atW1cvmFm3bh2MRiOAwExL7X835e6778ZHH32Er776qtFABgBGjx4NAA0GMwaDAQaDoVnPS51PiqF5b/nmHkdElCjcPgWnnF64vNEJYuJJRJ/Y99xzD+68805s2rQJ55xzDgDg+++/x6uvvorf//73AIBVq1Zh+PDhjZ5HCIF77rkHy5cvx5o1a9CzZ9PfhLdu3QoAyMnJiWTo1MnZm1mxsrnHERHFO49fCezc7MCfaxEFM4888gh69uyJv/3tb/jXv/4FAOjfvz9eeeUVXHfddQCAO++8E3fddVej55k9ezaWLFmCFStWICUlBWVlZQACy1hJSUk4cOAAlixZgksvvRRdunTBtm3bcP/992PChAkoKCiIZOjUyWkgQUIgP6YhUs1xRESJzKeoOFXtjau2A20lopyZqD15mO3dQKBA30033YQjR47gl7/8JXbs2IHq6mrk5+djxowZeOSRR5qd/9KSNTfq+H48UomrXl4Pn18NJPzWeUyq+dFpZbx9xxgmABNRQvIrKk45fXB42qf1QMLmzERLUy9yfn5+veq/RK0xNM+Kfllm7Ci215udqb3dL8uMoXnWM3+ViCiuKapApdMLe5z2T2pLEQUzsiw3OKsCAIrS8ZKLqGOQZQlXDMvFrhI7lDD/1mUJuGJYLgvmEVHCUNVA1V5bnPdPaksRBTPLly8Pue3z+bBlyxa88cYbrPFCcU1VBb7afwJGnQYur4K61RVkAEadBl/tP4FbxvViQENEca229UCi9E9qSxEFM+EK4V155ZUYPHgw/vOf/+CWW25p9cCI2sLOEjt2ldjh8auQZAk6CZAgQUBAFYDHr2JXiR07S+wY2o1LTUQUf4QQsLv9sDkTs/VAW4hqo8lzzz0Xq1evjuYpiaLqpMMDu9sHIQR0GglaWYZGDvyvTiPVfEj4cNLhafpkRETtrMrtw9FTLpx0JG7rgbYQtQRgl8uFv/71r8jLy4vWKYmi7pTTB1UVwSUkf01rA0kCNLIEWZagqgKnnL4Yj5SI6DSHx49T1V74FAYw4UQUzKSlpYUkAAshUFVVBZPJhH//+99RGxxRtKUm6yDLEvxKoLlkXT5FQAKg1UhITdbFZoBERHV0pP5JbSmiYGbhwoUht2VZRteuXTF69GikpaVFY1xEbSIj2QCdLNULZGoJADpZQkYyW2IQUey4fQoqqr1wR6l/UkcXUTBz4403RnscRO2if6YZnia+4Xj8KvpnmttpREREp7l9gdYDTm/Hr9obTRHnzFRWVuK1117D7t27AQCDBw/Gr371K1it3AFC8evD7aVh68vUpYjAcbNGNt70lIgoWrx+Faec3g7dP6ktRbSb6YcffkDv3r3x3HPPoaKiAhUVFfjLX/6C3r17Y/PmzdEeI1HUbD1cGdXjiIhaw6eoKK9y4+gpJwOZVohoZub+++/HFVdcgVdeeQVabeAUfr8ft956K+677z589dVXUR0kUbQY9c2L35t7HBFRJPyKikqXD1WdsPVAW4gomPnhhx9CAhkA0Gq1+N3vfoezzz47aoMjirb+WSlRPY6IqCU6c/+kthTR10+LxYLDhw/Xu//IkSNISeFFgOJXhtkATRNdCjRS4DgiomhRVYFT1V4cqXDC5vJ1mEDG7VPw3uaj+HhbaUzHEdHMzNVXX41bbrkFzz77LMaOHQsA+Pbbb/Hb3/4W1157bVQHSBRNXcwGJBu0sLsbXptONmjRhcEMEUWBEAJ2lx+VLm+H6p90zO7Giq0l+Hh7KarcfvTKSMbUIdkx62kXUTDz7LPPQpIk3HDDDfD7AxcFnU6Hu+66CwsWLIjqAImiaWB2SpNdZVUhMDCbM4xEFDkhBKo8flRWd5z+SUII7Ci2470tR/HN/hOoG5sdPFGNtfuP44L+mTEZW0TBjF6vx/PPP4/58+fjwIEDAIDevXvDZDJFdXBE0baz1A6Xt/EiVC6vgp2ldgzLT22fQRFRh1Ll9qHS6eswrQe8fhX/t7ccyzYXY3+5o97jSToN/uvsbuidEbv6XBEFMzabDYqiID09HUOHDg3eX1FRAa1WC4vFErUBEkXT5sOnmlVnZvPhUwxmiKhFqj2B1gMdJYipqPbig60l+HBbSdh+dTlWI6YX5uHac/LRvUtyDEZ4WkTBzDXXXIPLL78cv/71r0Puf/vtt/HBBx/gk08+icrgiKKtpNIV1eOIiFxeBRVOLzwdpPXAvmNVeHfTUazZexz+MHk+w/NTMWtEHs7t1QUaWUKKMfa97CIKZjZs2IC//OUv9e6fOHEi/vCHP7R6UERERPGuI/VPUlSBr/cfx3ubi7GzxF7vcZ1GwsUDszBjRB56d42/di8RBTMejyeY+FuXz+eDy8VvtBS/clKTonocEXU+Hr+CU9Udo3+SzeXDx9tKsWJrCY47PPUe72LWY9qwXFxekAurKfYzMA2JKJgZNWoU/vd//xcvvPBCyP0vvfQSRo4cGZWBEbWFYd2a1zusuccRUefh9auodHrh6ABtBw6dqMayzcX4YvexsM13B+WkYOaIbpjQNwNaTfxXRI8omHniiScwadIk/Pjjj7jooosAAKtXr8b333+Pzz77LKoDJIqmn044m33cyB7pbTwaIkoEPiXQBNLRSH2qRKAKge8OnsSyzcXYHKb/nEaWMLFfV8wckYeBOYm1kSeiYOa8887D+vXr8cwzz+Dtt99GUlISCgoK8Nprr6Fv377RHiNR1ByuqI7qcUTUcXWU/knVHj9W7izD8i3FKKl013s8NUmHXwzLwRXDchO2+nlEwQwADB8+HG+++WajxyxYsAB33nknUlNTI30aoqjacPBks45btbMM91/cv41HQ0TxSFEFbC5fwrcdKD7lwvItxVi5swzOMPW1+nQ1Y+aIPFw4IBN6bfwvJTUm4mCmOZ588klcddVVDGYobhyz1/9WEs6eMgfWFZ3A2D4ZbTwiIooXqipgryl411Sl8HglhMDmw5V4b/NRbDhYgTP/ClkCzuuTgZkj8lCQZ4Ukxab9QLS1aTCTyBEtdUwtqQMx/9PdWDF7XMx6jRBR++gI/ZPcPgWf7zqGZVuK8fPJ+rmBZoMWlw7NxvTheci2GmMwwrbVpsEMUbwx6rUAvM06dt8xB7YX21gJmKiD6gj9k85s+Him7ukmzCjMwyWDs5Ck08RghO2DwQx1KoYWrAt7/Sq2Hq5kMEPUASVy/6TGGj7WGtUzHbNG5GFkjzTIHWQpqTEMZqhT6Zdlxu6y+o3SwhEAlHorzkSUyKo9fpxyeuENU1sl3nn9KtbsLcd7DTR8NOpkTBmcjRmFechP71yNnxnMUKcyulcXrPixrNnHW+Og5wgRtV4i90+qqPbigx9L8OGP4Rs+ZluMmFGYi6lDcmA2ds7Lepv+1ePHj0dSEsvCU/w4cLxl9WPS4rh8NxE1ze1TcMrphSvM1uR4t+9YFd7bXIz/21PeQMNHK2YWdsOY3oGGj51ZRBvLN2/ejO3btwdvr1ixAtOnT8fvf/97eL2nkys/+eQT5OTkNHie+fPn45xzzkFKSgoyMzMxffp07N27N+QYt9uN2bNno0uXLjCbzZg1axaOHTsWybCJsKu0fgO1xmz8qaKNRkJEbcnjV1Bmc6Ok0pVQgYyiCqzZexz3vrUFd/57Mz7fdSwkkNFrZVw6JBuv3DASf7lqOMb1zej0gQwQYTBzxx13YN++fQCAgwcP4pprroHJZMI777yD3/3ud80+z9q1azF79mx89913+Pzzz+Hz+XDJJZeguvr0t+f7778fH374Id555x2sXbsWJSUlmDlzZiTDJgJaWC5g6cbDWFd0oo0GQ0TR5vWrKLe7UXzKlVCNIG0uH5ZsOIzrXtmA//loF3ac0bk6w6zHLePOwn9uOxcPTu4fl52rY0kSERSDsVqt2Lx5M3r37o2nnnoKX375JVatWoVvv/0W11xzDY4cORLRYI4fP47MzEysXbsWEyZMgM1mQ9euXbFkyRJceeWVAIA9e/Zg4MCBWL9+Pc4999x65/B4PPB4Tnf+tNvtyM/Ph81mg8WSWL0mKPquf/kbfHvI1uzjjVoZ5/RMxxs3j2K9GaI45ldUnHL64PAkVuuBphs+WjBrRB7Gx3HDxzSTHmnJ+qif1263w2q1Nuv6HVHOjBACas2e/C+++AK/+MUvAAD5+fk4cSLyb7E2W+Aik54eaPC3adMm+Hw+TJo0KXjMgAED0L179waDmfnz52PevHkRj4E6tp8qmlcBuFayQYMD5Q7sLLFjKDtpE8UdRRWodHphT6D+SU01fNTKEib2DzR8HJDNL+HNEVEwc/bZZwc7Z69duxaLFi0CABw6dAhZWVkRDURVVdx3330477zzMGTIEABAWVkZ9Hp9vXYIWVlZKCsLvyNl7ty5mDNnTvB27cwMEQDYWtj1NsWgRbVPRYWzeYX2iKh9qKpApcsHuytxWg9Ue/z4dEcZ3t/acMPHy2saPnZJ0IaPsRJRMLNw4UJcf/31eP/99/GHP/wBffr0AQC8++67GDt2bEQDmT17Nnbs2IFvvvkmot+vZTAYYDDwTUDhaVq4UuRXBXSyhHRT9KdQiajlavsn2Vy+hGk9cPSUE8u3lGDljjK4wmwNT9SGj0l6DcwGLZL1sd8OHtEICgoKQnYz1XrmmWeg0bS8XPLdd9+Njz76CF999RW6desWvD87OxterxeVlZUhszPHjh1DdnZ2JEOnTi7VKMPmbv7OhlMuH0Z0T8PgXE71EsWSEAJ2tx+VzsTonySEwKafT2HZluIO1fBRp5GRYtTCbNDGVQ5PVMMpo7FlzauEELjnnnuwfPlyrFmzBj179gx5fOTIkdDpdFi9ejVmzZoFANi7dy8OHz6MMWPGRG3c1Hm0dIemTgbuOr83k3+JYsju9iVM/yS3T8EXu49h2eZi/NRBGj5qZAnJhkAAY4zT/k7NDmbS0tKaHTlWVDSvNsfs2bOxZMkSrFixAikpKcE8GKvViqSkJFitVtxyyy2YM2cO0tPTYbFYcM8992DMmDFhk3+JmlIZpnpmYy4ckIWxfTLaaDRE1BiHx49T1d6E6J/U0Ro+SpIEU80ykkmvifuZo2YHMwsXLgz+98mTJ/HEE09g8uTJwRmS9evXY9WqVXj00Ueb/eS1icMTJ04MuX/x4sW46aabAADPPfccZFnGrFmz4PF4MHnyZPz9739v9nMQ1dXSdiw//HQK64pOMKAhakdOrx8V1fHfP6k5DR9H90zHzBF5OLtH8ycEYsmgCwQwZoM2oYrxRVRnZtasWbjgggtw9913h9z/t7/9DV988QXef//9aI2v1VqyT506vv5/+BieFiw16WQgL82EJ2cMZUBD1MZc3kDrAXec90/qaA0ftbIMc00eTDwlILd5nZlVq1bhqaeeqnf/lClT8PDDD0dySqJ20dIveooKVLn9WLT2AM7t1YW5M0RtIFH6JzXV8DHHasT0wjxMHZINsyH2O3waI0kSkvUamI1amOJgN1JrRfQXdOnSBStWrMADDzwQcv+KFSvQpUuXqAyMqC0oLZyHVBHYfsjCeUTR5/WrOOX0otoT320H9pZVYdmWxho+pmLWiDyc2yv+Gz4adYEAxqzXdqgvZxEFM/PmzcOtt96KNWvWYPTo0QCADRs2YOXKlXjllVeiOkCimFNV+FSwcB5RlPiUQBDjaGERy/bkV1R8U3QC720uxs6S+g1q9VoZkwZkYsaIvLjvk6TTyIE8GKMWujjaTh1NEQUzN910EwYOHIi//vWvWLZsGQBg4MCB+Oabb4LBDVFHYfMoSDFoWTiPqJUSoX+SzeXDx9tKsWJrCY47PPUezzDrMW14Ln4xNBdWky4GI2weWQpsp04xxu926miKeKFs9OjRePPNN6M5FqK45PEqGJ6fysJ5RBFKhP5Jh05U473NR/HF7vKwu6gG5Vgwc0QeJsRxw0dJkpBUs4yUnADbqaMp4mBGVVUUFRWhvLw82HSy1oQJE1o9MKJ4YTJoWDiPKALx3j9JUWsaPm4pxpYEbvio18pIMehgNibWdupoiiiY+e6773Ddddfh559/rhdlS5IERYnvjHSillh4zXBuyyZqASEEbK747Z9U2/Bx+ZZilNoSs+GjVpaRbAjMwhi0HX8ZqSkRBTN33nknzj77bHz88cfIycnpVFNZ1LlIAMb17hrrYRAlhNr+STZnfLYeSPSGjx1tO3U0RfRq7N+/H++++26wWzZRRyUAfLitFDNG5MV6KERxrcrtQ6XTF3etB5rT8HFcnwzMiOOGjx11O3U0RRTMjB49GkVFRQxmqFM4fLJ+hU8iCojX/klun4LPdx3Dsi3F+LmBho+XDc3GtMI8ZFvir+FjZ9hOHU0RBTP33HMPHnjgAZSVlWHo0KHQ6UK3pxUUFERlcETxYHcZgxmiM8Vr/6SmGj72SDdhxog8XDwo/ho+drbt1NEUUTAza9YsAMCvfvWr4H2SJEEIwQRg6nCS9PxWRFTL7VNQUR1f/ZMSveGjSa/tlNupoymiYObQoUPRHgdR3OrRJTnWQyCKuXjsn+T1q/i/moaPRWEaPibpNJgyJBszCnPRLS2+Gj7WbqdONmjitm5NIokomOnRo0e0x0EUt8b35bZs6rzisX9SRbUXH2wtwYfbEqvho0aWgnkw3E4dXRH/v/yvf/0LL730Eg4dOoT169ejR48eWLhwIXr27Ilp06ZFc4xEMVXlip8PcaL2Eo/9k/aWVeG9zUexZu/xsA0fC7unYmZhfDV8rLudOknHZaS2ElEws2jRIjz22GO477778Oc//zmYI5OamoqFCxcymKEOZcNPFZg4IDPWwyBqF35FRaXLh6o4aT3QrIaPAzMxszAPveKo4aNBp0GKUYtkfeetytueIgpmXnjhBbzyyiuYPn06FixYELz/7LPPxoMPPhi1wRHFg7V7y/HbS/qzvgN1aPHWP6k5DR+nD8/DZUNz4qbho04jI9mghdmgjcuiex1ZxAnAhYWF9e43GAyorq5u9aCI4snxKi92ltgxtJs11kMhijpVPd16IB76JzWn4eOsEXkYHycNH2VJgsmgQYpBhyQ982BiJaJgpmfPnti6dWu9ROCVK1di4MCBURkYUbwQQqDC6Y31MIiiSggBu8uPSpc35v2TErHhY5JeE0jmNWiZBxMHIgpm5syZg9mzZ8PtdkMIgY0bN+Ktt97C/Pnz8eqrr0Z7jESxJQHpJn2sR0EUFfHUP8nh8WNlIw0f00w6XF6Qi8uH5cRFw0edRkaKMRDAxMOsEJ0WUTBz6623IikpCY888gicTieuu+465Obm4vnnn8c111wT7TESxVSyXoPBufHxbZCoNeKlf1JzGj7OGpmHC/rHvuGjRpaCeTCsyhu/WhzM+P1+LFmyBJMnT8b1118Pp9MJh8OBzEzu9qCOqTqOioQRRaLaE2g9EMsgRgiBH34+hWWbi7HhUEW9x2sbPs4ckYehMW74KEkSTDXLSCZW5U0ILQ5mtFot7rzzTuzevRsAYDKZYDLFV2VFomg66fBiycbD+OW5gRwxVRXYWWJHhdOLdJMeg3Mt3OlEccnlVVDh9MITw9YDrpqGj8s3F+Pnivhu+GjQnc6D4XbqxBLRMtOoUaOwZcsWVgKmTkEAeGvDz7huVHd8d/Ak/r6mCHvKquDzC+i0EgZkp+DXE/tgbB9WCqb4EA/9k47Z3Xh/SzE+2VEW1w0ftbIMs5HbqRNdRMHMr3/9azzwwAM4evQoRo4cieTk0N417JpNHU1xpQtLNh7GC1/uR0W1F8EdrF5gw6EK7C/fiueuGs6AhmLK41dwqtoHpzc2VXuFENhebMOyLcUNNnw8t1c6ZhbmYWQMGz5yO3XHI4kIqiPJcv3oNV67ZtvtdlitVthsNlgsTOLs7M56+OOIfs+kk5FpMeJwhROSFPg2JyEwa+NXVQgBDM61YMXscVxyonbn9auodHrhiFH/pERp+Fi7nTpZr+W/0wTQkus3u2YTNYNXUXGkwgkJgE6Wg98oa2/7FBV7yxzYXmzDsPzUWA6VOpFY909qTsPHGYV5mBLDho/cTt05RPTu+vnnnzF27FhotaG/7vf7sW7dOubSUIejqIFZGL1Wqjc1LkkSNBoJfkXFliOVDGaozSmqwCmnN2b9k+K94SO3U3c+EQUzF1xwAUpLS+ttx7bZbLjgggviapmJKBokCYE8GYHAdMyZaj7PpdhXg6cOTKnTeqC9gxi/ouLr/YGGj7tK46/hI7dTd24RBTO1uTFnOnnyZL1kYKKOoPbLp18RkGUBqU5EIyCgqAI6jYzh3VNjM0Dq0FRVwF5T8K69+yfFe8NHvVZGilHH7dSdXIuCmZkzZwIIRMA33XQTDIbT5aUVRcG2bdswduzYZp/vq6++wjPPPINNmzahtLQUy5cvx/Tp04OP33TTTXjjjTdCfmfy5MlYuXJlS4ZN1Gp6jQRFCCgq4PGp0GoCy00SAt9YBYB+WWYMzWMzSoqeWPZPOnjcgWVbihts+Dg4N9DwcVyf9m/4qJVlJBs0SDHquJ2aALQwmLFaAx/UQgikpKQgKSkp+Jher8e5556L2267rdnnq66uxrBhw/CrX/0qGCidacqUKVi8eHHwdt0Aiqi91M68KGogcPEptWtOgEYGMs0GzJ06kDskKCqEEKjy+FFZ3b79k5rb8HHWiG7on53SbuMCAl+ik/WBAIbbqelMLQpmaoOKs846Cw8++GCTS0rffvstzj777AYDkKlTp2Lq1KmNnsNgMCA7O7slwySKOkUASp1vpzIAUbM3WyfLuGVcT9aYoahwePw41c6tB+K54aNRpwkUteN2ampERDkzjz/+eLOOmzp1KrZu3YpevXpF8jQAgDVr1iAzMxNpaWm48MIL8cQTT6BLly4NHu/xeODxnF7XtdvrJ6oRtZaKQLKvLAXqzHzwYwluGdeLH7YUsWqPH6ec3rBLOm2lqYaPfTPNmDUiDxPbueGjTiMH2goYtdBxOzU1Q5tu/G9ttv2UKVMwc+ZM9OzZEwcOHMDvf/97TJ06FevXr4dGE36acf78+Zg3b16rnpeoOQQCW0AVVWBXaVVI/yai5mrv/km1DR+XbynGhoMVOPNTWpaAcX0zMKuwG4bkWdptV5AsBbZTpxi5nZpaLjZVjJrpmmuuCf730KFDUVBQgN69e2PNmjW46KKLwv7O3LlzMWfOnOBtu92O/Pz8Nh8rdU5eRcCgleBVBJZuPIzrRnXn7Aw1i9un4JTTC1c7dWVvquFjilGLy4bmYNrwXGS1U8NHSZKQVLOMlMzt1NQKcR3MnKlXr17IyMhAUVFRg8GMwWBgkjC1K58ioJEklNnd2Flix9Bu3NFEDWvv/klltQ0ft5eFbXfQI92EmSPyMKkdGz7qtTJSDDqYjdxOTdGRUMHM0aNHcfLkSeTk5MR6KERBqgCSdIF1/QqnN8ajoXjVnv2Tgg0fNxfjm6L4aPhYu53abNTCoOUyEkVXmwYzTf0DcTgcKCoqCt4+dOgQtm7divT0dKSnp2PevHmYNWsWsrOzceDAAfzud79Dnz59MHny5LYcNlGLJek10GtkpJv0sR4KxRm/ouKU0weHp+1bD3j9Kr7cU45lW+Kj4WPtdmqzUQuTPqG+O1OCiWkC8A8//IALLrggeLs21+XGG2/EokWLsG3bNrzxxhuorKxEbm4uLrnkEvzpT3/iMhJFRG3DomNev8DAHDMG57IzOwW0Z/+kkw4PPvixBB/+WIpKV+wbPnI7NbW3Nn1XV1VVNfr4xIkTG/1HvmrVqmgPiTqxnSVtt00/2aDBXef35gc3QVUFKl0+2F1t33pgT5kdyzYXN9jwcUT3VMwckYfRPdu+4SO3U1MsRRTMHDt2DA8++CBWr16N8vLyegEJG01SPGrLfJZJA7qyaF4n1179k5rT8PHigVmYOSIPPTPatlcet1NTvIgomLnppptw+PBhPProo8jJyeF2OkoIbZnP8uaGI+jeJRm3TejdZs9B8UkIAbvbj0pn2/ZPsjl9+Gh7CVZsLcEJR/3AvKvZgGnDc3FZQQ6sSW3b8NGk13I7NcWViIKZb775Bl9//TWGDx8e5eEQtZ22zGdRASz4dA8G5lgwrm/XNnseii92t6/N+ycdOlGN9zYfjXnDx9rt1MkGTbs3liRqSkTBTH5+fpsntBFFk6qKNs2ZAQL9m3737jZ889CFzJ3p4Nq6f1JzGj5eMCATMwvz2rTho0aWgnkw3E5N8SyiYGbhwoV4+OGH8fLLL+Oss86K8pCIomtd0QksWnsAB8JsVY22Upsb245UYniPtDZ/Lmp/Tq8fFdVt1z/J4fHj0x1leL+xho/DcnHFsFykJ7fNsmnd7dRJOi4jUWJodjCTlhZaWKm6uhq9e/eGyWSCThe6PltRURG9ERK1wrqiE/j98u1wePztUt1UAPh0Z1mbBTO1M0wVTi/STXoMzrVwFqgdtHX/pCMVTizfUoxVO4+FbfjYp6bh4wVt2PDRoNMghdupKUE1O5hZuHBhGw6DKPpUVWDR2gNwePzIthjbpfIqAFS3UZn6ujNMPkVAp5HQO9OMu87vzZ1UbaQt+yfVNnxctrkYGw7V/wJY2/BxZmEehuZZ22SGRKeRkWzQwmzQtmtXbKJoa3Ywc+ONN7blOIiibmeJHQfKHUgz6SFJErRy+3xYF+a3flbmzBkYm8uLR97fAYfHjzSTHnqNDK+iYndpFX6/fDuenDGUAU0UefwKKp0+VLdBABzrho+yJMFk0CDFoEOSnnkw1DFElDPzySefQKPR1Gsr8Nlnn0FRFEydOjUqgyNqjQqnFz5FQF+z88Kob/tgRpaAacNyW3WOcDMwTp8CVRXonm4KfkM3yhpkW2SU2T1YtPYAzu3VhcsDreRTVJxyeuFwRz+IabLhYxcTZha2XcPHJL0mkMxr0DIPhjqciIKZhx9+GAsWLKh3v6qqePjhhxnMUFxIN+mh00jwKiqMsgYS2v4DXBXAEx/txgWDMiPKaamb41M7A2N3B6rJamQJ1V4lpBy9JElINelwoNzBjt2t0Fb9k5pq+CgBGN2GDR91GjmQB2PQcjs1dWgRBTP79+/HoEGD6t0/YMCAkMaRRLE0ONeC3plm7C6tQrZFbrdvo2989xM+3lECnUYO5rSc26tLk4m7Z+b41I5XIwfCMFUIHK9yI1mfHPK3GDQybKpgx+4IKKpApdMLe5T7JzXV8NGk12DK4GzMKMxDXlpS1J4XCLxfavNgWJWXOouIghmr1YqDBw/W25ZdVFSE5OS2LZ9N1FyyLOGu83vj98u3o8zuQaqpbaui1hIAdFoZGknCtiOVuPutLci1GlFR7W00cffMHJ9aWlkOBj4evwq3Tw3JdfAoKnSyxI7dLaCqAjaXD7Yo909qquFjbmpNw8fB2UiOYsNHSZJgqllGMrEqL3VCEf1rmjZtGu677z4sX74cvXsHyrcXFRXhgQcewBVXXBHVARK1xtg+GXhyxtB2qzNTq9zuASCgqoHqwKeqvcixGpCZYmgwcffMHJ9aRr0Mg1YDl9cPSZJqqs0GghkhBCqdPgzMSWHH7mYQ4nQQE83WA7tLaxo+7jse9rxt1fDRoDudB9PWjSSJ4llEwczTTz+NKVOmYMCAAejWrRsA4OjRoxg/fjyeffbZqA6QqLXG9skILvNc/rdv2uU5/aoIydARAMqrvDDoAv1srEYtjju8eHrVXrx7Vjq0Wrlejk8tCRK6phhwpEKBKgT8qoCqCngUFZVOH8x1OnazDk14tf2TbM7otR7wKyq+2n8Cy9q54aNWlmE2cjs1UV0RLzOtW7cOn3/+OX788UckJSWhoKAAEyZMiPb4iKJClqV2T4498/u5KgRKbS5oJMCrCKgC2H60Ele+vB6/m9wf5/bq0mCOT7Jeg2RDYPlAUVSUOzzQyRIG5qQEl6tYhya8qppO1j5FhSoEio5Vw+b2wmrUo09WMuQWLsnYnD58vL0U728tbrDh4/TCXFw6NHoNH7mdmqhxkmhh1pvP50NSUhK2bt2KIUOGtNW4osZut8NqtcJms8Fi4TR8Z3fWwx+3y/NIEnDmvyyNDChqYPu2TiMDEPArAmaDFmnJejw5YygA1OxmUpBq0sGgkUNmYJ6YPgTWJH29mZdwu6C8NTt0zAZNXNehaavZpDP7J205fApLNh7BkZPV8KkCOllCfpdkXDcqH4Xdm64NdPC4A8s2F+OLPeEbPg7JtWDmiG4Y3zcjaks+tdupk1mVlzqhlly/Wzwzo9Pp0L17dyhK25T1JuoQRGDbbW08IyEQyACARpIgSxJUAcgykGE2wOb2Y9HaA3jj5lEhOT62motu3RmYMzW0CyoR6tC0xWxSuP5JWw6fwl8+3wenV4HFqINFI8GnCBw87sBfPt+HORf3CxvQ1DZ8fG9zMbYeqaz3uFaWMLF/V1w5shv6ZUWn4SO3UxO1XETLTH/4wx/w+9//Hv/617+Qnp4e7TERJTwBhOTMyFKgqzYQWPISEFBUAaNOgySDBpIsBWvF1M3xac5sRUO7oID4rkPT0GxSpFWN3T4FFdVeuM/obaQKgSUbj8DpVZBh1gfrDRm0EjLMepxweLFk4xEMy08NLjlFo+FjS5a0uJ2aqHUiCmb+9re/oaioCLm5uejRo0e97dibN2+OyuCIElndVabaQEZCIBk1sNwUSOyVINWrFdOSHJ+GdkHVisc6NNGcTXL7Aq0HnA30xCo6Vo0jJ6thMerqFU6UICHFqMORk9UoOlaNJL3caMPHvjUNHyc20fCxOUta3E5NFD0RBTPTp0+P8jCIOqba/Bi/okKps/Rk1GnQNcUQrObbmloxDe2CqhWPdWiiMZvk9QdaDzTVP8nm9sKnClg04YMFnQxU+BQ898Ve7D1Wf/t+bcPHWYXdMCTP0mTQ0dSS1kNTBuD8/pncTk0URREFM48//ni0x0HUIakCMGhlnN0jDWV2N05UeZBhNgSWlmpmCVpbK6axSsfxWoemNbNJLe2fZDXqoZMDAYVBe/q1UYWA3eVHhdMLvyrqBTKRNHxsaEnLqJORpJNx3OHFO5uO4hcFuXGXv0SUyKJXgpKI6jFoZPTqasYbN4/Cxp8q8Pvl22Fz+yHJUr2dSrW1YloqXKXjaJ27rUQym9RY/6TG8lP6ZCUjv0syDh53IMOsh18RqAxW/60/ttY0fAxZ0qpJ9NZIUvC1T0vWx2X+ElGiiyiYURQFzz33HN5++20cPnwYXm/ot6eKioqoDI4o0flUFUcqqrG7rKpeNeLm7FRqrrY8d1toyWxSU/2TmspPkSUJ157TDQtW7sVPJ53wKfXPEa2Gjza3F34VSNJpoNVI9c4T6/wlFlWkjiqiYGbevHl49dVX8cADD+CRRx7BH/7wB/z00094//338dhjj0V7jEQJSxXAKacPJ6o9ANDinUot0dxzx8MFrTmzSXdM6NVk/6Sm8lPuubAPKqp9WLY5fIE7WQLO690Ft0/o3aqGjzqNDLNBi36ZKTDqZChCQCfVX0KLZf4SiypSRxZRMPPmm2/ilVdewWWXXYY//vGPuPbaa9G7d28UFBTgu+++w7333hvtcRIlLFUAGw+cxAX9MwO3VYGicgeKK53ISzVhYHZKMJhobaDR1C6oeLqgNTSbNCDbjBvGnIX8dBNONTKD0diW69QkLUptHjzy/k74w6wldUnW48IBmfjlud2RYoysSq8sBbZTpxhPb6e2JuniMn8p2tvgieJNRMFMWVkZhg4NVCs1m82w2WwAgF/84hd49NFHozc6og5i1a5j+O2UAXjtm4N4cc0BVLl8UAHIAOZ9tBOzJ/bG4FxrmwYa8XhBqzubdLLaA4NWgxyrEaoQTTaCDLfl2lWzTbuqgR1OrW34KEkSknQamI2BHltnLiPFY/5SuG3wQggIEWiTUen04e9riuKyqCJRc0UUzHTr1g2lpaXo3r07evfujc8++wwjRozA999/D4PBEO0xEiW8Ew4P/vTxLvxr/c9QVAGtRoJWCsza2Jw+zP90D5INWhi0cpsEGvFcJViWJZyVYUKqSRfsn9QctVuuU2TAXtN/yR2mzYBWljB5cHarGj7qtTJSDDqYjU1vp463/KUzt8E7PH4cr3LD41eDLTc2HjqFJRsP45fn9mjXsUUqHpZKKb5EFMzMmDEDq1evxujRo3HPPffgl7/8JV577TUcPnwY999/f7THSBQVahPf9Nv0uYXA298fgaIK6LUSZCmQV6GoKgQCfZyq3H4oukARN6NOimqg0R5VgiO5wFR7Aq0HavsntYQGMrx+FT+ddEEJEwBpZAlGrYx5VwzBiB6pUIXAvjJHs5tMamUZyYbALIxB27JdTW2ZG9VSdbfBOzx+FJ8KvF5aWYIkASoEfH4VL3y5H70ykuN+uSmelkopfkQUzCxYsCD431dffTW6d++O9evXo2/fvrj88sujNjiiaNpZYo/Zc/v8Knw1MzK1gYzPrwbbHtReit0+geJTLuSlJcFs0IYEGtuLbZAlKaKLY1tXCW7pBcbp9eOU0wdPmCq7TTlw3IHlm4vxxe5yeMMEQUadjNQkHdw+Bb0zUzC8uzX8jqd0E8b364ocqzEY3GhkGcn6QABj0reuckUsOrWHU7sN3uNXcLzKHUhOlk/vtJIEoJEFPH41bnt41YrHpVKKD1GpMzNmzBiMGTMmGqciajPHq+r32Gkv/pocBVkCBAT8Sk0gUzeSQeACqAqB41UeJNcU1jNoZJR7/Lj/P1tR6fRCFUCSTkafrOYtW6iqQIXDGygS5/bBaqpf1r81u2zWFZ3A3GXbYHP5YNIHEmJlCWEvMA31T2rK6YaPR7H1iC3sMSa9BukmHWRZRpXbh2SDFteNysePRyrr7Xiyu3348WglthyphEkfyH/p1TUZsy/og3F9u7b4NYhntdvgtx+1weNXa2Zkago2BnuEaZFhju8aOPG8VEqxF3FL1n/9618477zzkJubi59//hkAsHDhQqxYsSJqg4tnqiqw/agNa/cdx/ajtpguYVDT1hWdwIKVe2P2/IoaiFl8SiCoEQII93ErSYHlEY9fgdsbmHUos7tgd/tx6GQ1bG4/qj1+2Fx+/HjEht8v3451RScafN51RSdw4+KNeHrlbtjdPhRXunDoeDUcdRJka3fZ9M40t3iXjaoKzP90N0psblR7FZRXeXDklBNldjfMBg0cHgWL1h6Ay+tHmc2NkkpXiwIZh8ePdzYdxQ3/2IhHV+ysF8ikmXS4ZFAWCrpZYdZrUO1T4Pb60aurGXMu7odh+alYsvEIqj1+mA1a+FUVdrevJrgDIABVVZGSpMX+8mo88v6ORl/PRFSblGzQyvCrIpD8CwFVCPgVEewRZtBo4Gvh7Fx7fg62ZKmUOp+IZmYWLVqExx57DPfddx/+/Oc/Q1ECH06pqalYuHAhpk2b1qzzfPXVV3jmmWewadMmlJaWYvny5SF9n4QQePzxx/HKK6+gsrIS5513HhYtWoS+fftGMuyo4ZptYqmdmq6ojn2jRVXg9PJSzedx3Y9/CSKw20RF4MLrUnCy2gcA0GmkwMVXAB6/Ar+qoqIaDX4brTclr9WgxOaC06vgSIUTeWlJ0GnkZu2yaSgfZsnGw9hVWgUIAa1GhhQYIlw+FSWVbnQx67CvrApr955Av2xzs1+nIxXORhs+9ssyY+aIbpjYryv0WrnBCsD7yhw4UF4Ft0+Fw+OCEKebfupkCbJGgk8VgJCQbTF02G/3Y/tk4J6L+uJPH+2CoqpQlcD7r26PMJdPadHsXHt/DiZiQ1VqPxEFMy+88AJeeeUVTJ8+PSR/5uyzz8aDDz7Y7PNUV1dj2LBh+NWvfoWZM2fWe/zpp5/GX//6V7zxxhvo2bMnHn30UUyePBm7du2C0di8XinRxjXbxFJ3arqr2QB7M/v5tIXaFaXaLI8zc1ZlKXChVRUVqggkx9bWWZElwF87q1NzvKIISJKComNV9ZYGwk3JG3UayLKEcrsbLp+CkkoXupoNTe6yaeiidceEXnhr42GowaRmKfh3aiUBnxrYjmzUaWBzN32BEULgh59P4b3Nxdh4qH4VcVkCxvftilkj8jA4N7ThoyxJYYOlTYdPwebyQ5ICu5oERDCY8asCWinw/4NfVSFJmqgkQser60Z1x8odZdhRYoPVqIVOo4FRL0OC1OIaOLH4HEzEhqrUfiIKZg4dOoTCwsJ69xsMBlRXVzf7PFOnTsXUqVPDPiaEwMKFC/HII48EZ3r++c9/IisrC++//z6uueaaSIbeKlyzTTx1p6YNuohXVaNCIHBBPnMmXpYCyyVajYzjVZ7gxfaEwxsMXBqavff4VVR5lOC30doZlE2HT2FPaRVSTbqQi77ZoEVyRjIqXT44PX78dvIATBvecNPDxi5av313G5xeBYEvyqebZgIAJAkaOdDZ2qjTwGps+ALj8in4bOcxLN9SjMMVznqPR9LwEQB0soz1BwNLRlpZgkaWa2rXBMJJgUCAqJEDO5eAjv3tXpYl/Hpi75r/PxWkmjQQKuBWlBbVwInV5+DgXAt6dTVHJRijjieiYKZnz57YunUrevQIrUmwcuVKDBw4MCoDO3ToEMrKyjBp0qTgfVarFaNHj8b69esbDGY8Hg88Hk/wtt0evfXT9tjeStHV1NR0e6sNZgZkmVF03AG/CkAAlS5/sEicVgbSTQa4/UqzZpI8PgWpSTqsKzqBv68pwp6yKjg9Clx+BU6vD5mWwM6oWpIkwWrUweNXkWbSNbh9uKmL1uEKF1w+PwxaDdw+BVqNhNBMoMAsSIY5sOxzpjKbG+9vLcYn28tCcnhq9ehiwqwReZg0MCtYYbcpGlmC2aCF2ajFvjIHKhxeGHUa+BQVMgQk6fQIa1JmghdFoON/u49GDZxYfQ5+d/AkbC4vqtx+2Jw+aGTAoNUg1aSHx6/GZUNVaj8RBTNz5szB7Nmz4Xa7IYTAxo0b8dZbb2H+/Pl49dVXozKwsrIyAEBWVlbI/VlZWcHHwpk/fz7mzZsXlTGciWu2iSe4LVVR4Ha3vJZJtAkRyLo/Ue1FskELr1/ApyghJfetSXqYDJpGS/nXpQiBH49W4sX/K0JFtbcmwTiwJFXtVXGkwon8dFNIQONRVKiqwMLV+1Fud4fNeWjqomVJ0sLh9sGYFKj34lcEZDmQiyEEoCiB4O2ygtzgEpQQAtuO2vDe5mKsO3Ci3oyTBGBInhWje6ZjZPc09M02N1oLpnYstdupk3Snq/LW/nvNtBhQWumumYWpqa1S53ktSdpO9e2+tTVwYvE5WHeGMNtiQKXTB49fhdOnwG13Y1BOCuZOHcgl/k4somDm1ltvRVJSEh555BE4nU5cd911yMvLw/PPPx+T5Z+65s6dizlz5gRv2+125OfnR+XcXLNNPINzLehi1mNPWVWT5fHbgyICSx4QQH6aCZACMxR1myCerPY2O5ABAgHAa18fxPEqT01uiBxIFFZEYClFFSizudA70xy8aNdWgD1SUY30ZEPI8tHcZdtw24TeqHR64fQGZn3OJIRAsk4DWQY8XhWZFgNOVfvgVRSoNTGjLAO9MpLxi4IceP0qVu8px7LNR3HgeP2laJNeg5HdU1Hh9OGYzYX3Nh/FB1uLQ7pfn8mg0yDFqIVZrw17Ia7996rXyMhLS8LxKg88fqVmW3rgvaCRJCTrtcE2CJ3l231rauC09+dguBnCNJMebp8Kn6LC5vLBmqTDub26ROX5KDFFFMy4XC7MmDED119/PZxOJ3bs2IFvv/0W3bp1i9rAsrOzAQDHjh1DTk5O8P5jx45h+PDhDf6ewWBos5YKtfUa4q2JHDXsu4MnUV7liYtABgjMypj0GqQlny4tX+n01Tuudrga6fTum7Dnq9k9dPSUCxICeSKSJAGSBB0EfDX1bFw+FdUePzSyjFPVHrh8CrSShNQkPQzawO8YZQ3MBhXFlS786aNdMOk1qHL78JNfRZbVCLNBCyEEVBGo++LyqzAbtNBp5ECzxxQ9VAF4fQpcfhUWoxbXjOqO19f9hI9+LEWlq/7fmZtqxIzCPORajfj7mgMNdr+ec3E/FHZPg04jI9mghdmghV7b+NJh6L9XA5IzTHB7VfhVFR6/ghMOLyQEas7oNXLM2g0kmvb+HAw3QyhJEpL0GiRBA51WxsHj1Vze7+QiCmamTZuGmTNn4s4774TX68UVV1wBnU6HEydO4C9/+QvuuuuuVg+sZ8+eyM7OxurVq4PBi91ux4YNG6Jy/kjEYxM5aljtNzpFFeiebsJxhwfVnpZXnI2mril6uP2BKXpVqCizuYO9iOrmctRqbGFMK9fsiJKkYJuEuheWQA8hORjQHK/yQq8JVBn2+QX8ksDPFdU123MDibUllW6oqoCQBFJNRrh9Klw+BcWnnMixJsGo0wTrlFS5feidmYJrzumGpd8fDVTXFQI6SUJeahKS9Br8+ZM9YQPJkd1TMXNEN4zulQ4AeOi97WG7X2eY9Tjh8OHtH45i6pAcmAzN/8gK++9VKwMK4PAoyEtNwq3jeyE/3cT+Pi3Q3M9BANh+1Nbqdg5c3qfmiCiY2bx5M5577jkAwLvvvousrCxs2bIF7733Hh577LFmBxsOhwNFRUXB24cOHcLWrVuRnp6O7t2747777sMTTzyBvn37Brdm5+bmhtSiaW/x1kSOGlb3G51Rp4HZoMWOGBfUsrn88KkCZXY3nF4lpIZKuAmYM7dv1y0YLETgG6peG5gZURQBIamQZEBGILDRyFLN1mOgX6YZO0rsweCmtnif0xsIVmRZCvTs0UiBIn8CyLYaceRkNbxKYMzdUpPgUwOBjEmvCS4BFXZPw95SB745cBzfHazAnrKqen+LQSvj4kFZmFEY2vBxX5mjXvdrIHDRlCUZ6cmBujMHjle3+Js3/722jaZeVwC4cfHGZtegaayvF5f341c8NfyMKJhxOp1ISUkBAHz22WeYOXMmZFnGueeeG6wG3Bw//PADLrjgguDt2lyXG2+8Ea+//jp+97vfobq6GrfffjsqKysxbtw4rFy5MmY1ZmrFUxM5atiZ3+jOTGKNBbdfhYRAXkzd75lndDWAjNOzMhJOF9irneSQgODyUO3MjiICycBQA8X3dBoZshz4HY0M7CgNBDLamiUc1DynEDWViRWBwMqNDEkKVIbVaWRkpwbyTXx+FeUOD5K0Mnp1NQcDGZvTh4+2l2DF1pKQ3J9amSkGTBuei8uG5sASJv+mtvu1RRMIwGQ5kMsS3D2l1cDu9kf8zZv/XttGQ6/rdwdPtqgGTVPF97i8H5/irXisJMSZ3/2aVlBQgFtvvRUzZszAkCFDsHLlSowZMwabNm3CZZdd1uhuo/Zmt9thtVphs9lgsfDN3plsP2rDHf/6AckGbXBr7/bi8H194k3dejTWJC2cHgWqEDW1aiSkmXTwKiocbj9UEX5WBwgERQKB5ou1fXkgSfD6G9/ZZdQGGjHWzpQoNbNJ5/XpgpE90nFh/0z8VFEdaPi4pzzs+YbkWjBzRDeM75tRs+QVXlG5A4++v6PB5o4unwKnx4+X//ts5kTEOVUVuHHxRuwutYds5wcCgUeZ3YOBOSl44+ZRkGWpwTpGp2qWq2oDn9PHKWGXtVistH019/+31mrJ9TuimZnHHnsM1113He6//35cdNFFwSaTn332WdhiekSx0Ng3unimkSR0TdGjyuOHTpZh0msASHD7FAgBJOk10GnkwOyKFAh8ZOn0bEtdAkCaSQufipqAQ4KviUAGCMzUuHwqTDoNnD4lODOzrugkNhyswN++LEJVmNowWlnCBQMyMWtEHvplpTR4fkmSYNIHlv56pJvwr+wU7C6tCtlaDfCbd6JpSQ2awbmWZhffa85yYTwteXRk8Vo8NqJg5sorr8S4ceNQWlqKYcOGBe+/6KKLMGPGjKgNjqg1wiUqJgJFCFS5/eiaYsAT04fAmqRHhdMb3CJd6Qo0Snzi452QEMiLkSUJkhToxl0331arkXD5sDx88GMJJAnwKk0HMtqaHVIVDi9g1qGs0g1/zZZyt18NqYlTK82kw+XDcnHFsFykJzecu2CoyV0yG7QhszVMrO8YWpKs29Lie40tF8bbkkdHFq/FYyMKZoDA1una7dO1Ro0a1eoBEUXTmd/oEoUE4InpQzCub9ewj6/ddxx+5fSxQGBGR9bKwcRev6rCpNeiZ1czknQaONx+NLwgdZoqAjViPH4FZTYF/jq9jM5k0mtw94V9cGH/zAa3SmtlGWZj49upmajbMbQkWTeSXUrh6uOwX177itfdZREHM0SJou43usv/9k2sh9MsGllCirHhmaR0kx46rQR4A+FJbUAjoabCLQRkCdBrZBTmp6KLWY9Sm7vR56xNOpYkQFVrEpAbiH3MBi2S9RqoqopeXcz1ghRZkmAyaJBi0CFJ37xWBLFK1OXyRPS0JFl3Z4m91buU4nXJoyOL191lDGaoU2hNxdNYsLn9uPetLbhtQi9cN6p7SL+knSV2nHR40C01CZVOL/yqgK6mUaIAoAoBpaa1QP/sFAzOaV6uSe0CVENF+mQJsCbpkJqkg04jQxUCJ53ekI7YSTV5MMkNVOVtSnv//8TliehqSS2u5gQ+A7JToAqBtfuOhw0043XJoyOL191lDGaI4tThCice/2An/vP9YcydGmjgWvfCqwoRTPz1qmqwcWItrSTh/H4Z2Flqx3G7B5kpehx3eBvswN0QjSyhS7IeFqM2pE+SVwkUyOuSbEB6sh5mgxbaOGno2RxcnmgbzV0ybCrw0cqAzeXFXf/e1GCgGa9LHh1ZvBaPZTBDFKf0Wgl+RWB3aRV+vWRzcNdS3QuvX1Whevw4c4OSQSvBoJXxz/U/47jDC4+iokuyHjqtjDKbG341tJZNOEZt4DkgRL1ARgBwuP0YmJOCCwdkJtwUPpcn2lZzlwwbCnxyrAaUV3lQanM3GmhGe8mDS47NE485bgxmiOKQVpYgIEGFgFBFsH+TSScjxaiDQRtI8rUadTU9l4CuZgPkmp41eq0MRRE47vDgq33HoZUC262NWg1SjDpUOn0NBjJaOdBGwGLUodLlwwmHB8fsHqQl62HUyvCrAjaXD5YkLWZf0KfVH/axuIBweaLtNXfJ8MzAJzVJh2dW7UGpzd1koBnNJQ8uObZMvBWjZDBDFKd8frVe/m2g07WzpuWAgKqKYI6LLAcaRypqIGcGAFKMOlQ4PLCY9Cg+5YJPURvMibEYNbAY9UjSy8FieRajFi6vgm5pgfwcl0+BTpYwKNcSlQ/5WF1AuDwRX+oGPtuP2nDweDXSamZTXF4FflWFVpZh1Mn1As1oLHlwyTEy8ZSLyGCGKA6F2wYNAJACj/lrGkvKUqCfEgAcs7kDu4h0p6fbVVVFpcuH49Xeen2egEA+jKoKZJh1SDOd7jYvSRJkCVAUgRSjFgtmFUCWpKh+A4vlBSRed2QksmjNsNUGml6/ilKbCx6/WtOHLNDCo0uyAb46gWZrlzy45NgxMJghSiBqnbUhCRIgnY5QagvdGVMNcHgUVDp9cDdQ7VevlWHWyejZNQVVbi+OO7wQENDIcqBejSxBCAGbI5AXMzTPGtUP8lhfQOJ1R0aiiuYMW7pJD1WoKLEFktW1cqDcgADg8qkosblgMWpDAs3WLHlwybFjYDBDlEDqTq6oqggJZmQAbr+CQydcwdmaujJTDLhiWC4GZlvgFyqsRj36ZCVjd4kdz6zai4pqH9KS9dBoJLh8SpvuTIj1BSRed2QkomjPsA3MToEiAL9yevYRCNRS0soCXn9gaXVgdmi7jEiXPLjk2DEwmCFKEHWbTwKAgIBQTzeT9IvTj9TVM8OEG8achXF9Tjd81GlkpBi1SDZo0SczBV3MhnbdmRAPF5B43JGRaFRV4O9rDqDS6YM1SRtYDpJbN8O2u6wKGinQpkNRAcgiMDMjAEUNLI1qJAm7y6qiEuhyybFjYDBDlCCS9BpUe5Tg7drApqFdSenJetwwpjuuGJYHIHARMOm1SDGe7iJeq713JsTLBSTedmQkmiUbD+P7nyqgqCocHn9NXosGXVMMMBu0Ec2wVTi9kCUJeWlJOOnwwuNXINRAzoxRp0EXsx5OrxK1QJdLjh0DgxmiBFE3kAEQtvidLAEX9M/E5EHZGHFWKjSyjCSdBia9Bj+dqMZPJ50NXrDbc2dCPF1A4mlHRiJZV3QCL6zeD29N4BnIswLcPgXFp1zIS0uCSadp8QxbbaCr18g4K8MEt1c9vZtJL8PtU6GT1agFulxy7BgYzBB1IEatjEqnFwadjK5mI8xGLTYcPBkX9TPO3O1yx4ReeOT9HbyAJKDaBG6PX6lJ0JWCfcEkTSDf5XiVB9lWQ4tn2EIDXUNNb6/A7F1bBbpcckx8DGaIOggJgTo0Px6txJOf7kbXlMBW63ion9HQbpfrR3fHV/tP8AKSYGoTuDPMBvjtbrh8KnQygJrcFkkCXF4/TlQFZr1aEnjEaqaES46JjcEMUQegkQJJvQKAX1VxvMqDJz/ZjVSTPuz25yyLhOJKN/78yW48OWNoRFuvm1tXpLHdLkcqnHhi+hBYk/S8gCSQ2gTuQH6MMViQURWh6edOrx8T+ma0+P/PWM2UcMkxcTGYIeoANLJcM9UP6GQZPkXF3mNVsBp1SEsO3f7s8PhxvMoDt8+P3aU+3PLG9xiY07KKvs2tK9KcejIvf3UQb9w8igFMAqmbwG02aJGerMcxu7texWqdRsabGw5jcK61xQEIZ0qoJRKnxS0RhSUhMK0fvC1J0Ggk+FUBt18N2f7s8PhRfMoFt0+BLAVqeOg1cnDZaV3RiSafr3amZXepHckGLTJTDEg2aMOeoyX1ZOKJqgpsP2rD2n3Hsf2oLVDTh4Jq81pOOX1QRWAnkywFmqPq5ED16GS9jLO6mODwKFi09kBEr2HtTMn5/bpiaLfoFm6MFr5X4gNnZogSnCSFBjMAgnP9Gun09meBQFKmKgS0msDOEwkCJr0W6brm1QRpaeXeeKgn01JsONi0unktxZVuuH1+aORAErACAa0sI9OSBFmu30upI+F7JX5wZoYowWnl040hgUAxPUUNBBA9M5JxyumDEAJurwqPXwkUzhOBHk8GbaB5X3NnSVo601J3OSKceCtI1pJZpzN1tm/otXktealJUAVqfgSSdDLy0pJgNgS+Kxs0ckgvpY6iNe8Vij7OzBAloLphhKKqkCQ5WCXVrwS6bffLMuPByf2C25/1mkBTSUkD+NXArE3XlNOzK82ZJWnpTEs81ZNpSmv6RXXWb+i1Ac0tb3wPvUaGSa8NBse14i1gjYZY9xaj+jgzQ5RgNDW5Cd3TTUhL1kOWJSiqCp9fhaKqkGUJmSkGzJ06EOP6dsWTM4ZiYE4KfIqAQKBZ5ZnfnoHmXXRaOtNSuxxhNmhQZvfA5VOgqgIun4Iyuyeu6slEmt/TnG/oHXnWZmieFQNzLPAqol4gUxuw9s40x0XAGi2JmgvWkXFmhijBKAJQ/AIllU6Y9DroNTLUmtoeeq2MwTkWzL6gT3BGoHZXyPZiG/6wfDuOnnIhL9UIWT79Xaa5sySRzLQkSkGySPJ7mvMNff6nu2FN0uHg8eoOOWvTGSvoJmIuWEfHYIYoQflUwOb2BW/LEqCTpfrJwAhccIblp+LhqQPw23e34cgpNyxJWlgMWnhV0eyLTqQXrkTYZhtJv6imvqEbtDJ2lVYhxRCoxxKrooVtLVEC1miJl95idBqDGaIOQhWAze3HliOVeOCdH4OdsmuDhnVFJ/DyVwfh9atw+fyo9vhwTJZgMWoxKNfa7ItOpBeueC9I1tJZJ1UV2PzzKVR7FRh1gd1iZyZiVzq9UIWANUkfbO7ZUfMqEiFgjZZEygXrLBjMUKfRkfIUGlPtUVDtUfDsZ3vxz3WH0CcrBRP6ZuDNDYfh8PiRnqxHlsWAKrcfdpcPeq0Gd0zo1aJvzx3xwtWSWafahN/dpXZUuX2o9vhg1GmD3aIBnN49JkmB6sxCwO073TTRmqTtcFuW4z1gjZbOuLQW7xjMUKfR2ZLxFFXArwrsLrXj+58qoNfI6J5uCn6LTDXpYU3SBavwju3dsrLzHfHC1ZxZp7rtGVKTdHB5Fbh9ClxeP4pPqcHEap+iQFEBk16GX1Xx00kXPH412LtIr5Gh1cjMq0hQnW1pLd4xmKFOozNeNOweP/IsRlRUeyGjfqBy5s6LlgYnze3PlEgam3UKl/CbaQn0JlJEYDdZud0NTaoRNrcfsiwhSSejpNINRYiaDtOBmoZunwL4VRypcMb6T6YIdcQZykTFYIYSWksupp0xGc/nU+Hyq5AA+BQVbp+KJH1owmKkOy/OrK0CAJkWA64d1R3Xjeqe0B/oDc06hUv4NRu0yEtLwvEqN9w+FS6fApvTjyG5VlQ6Pdh7zAFVCOjkurkVgddLloCVO8oS/vXqzDriDGUiivtg5o9//CPmzZsXcl///v2xZ8+eGI2I4kW4QmW9uiZjypAc5Keb6gU3A7NTYjzi9qcCcHn9wf/2qyqAQLKq2xvI31BqpsdbEuzVXWoxaDVwen3w+BWccHjw+Ac78Z/vD2Pu1IEdbqq9oS25ZoMWyfpkOL0KTlZ7MfvCPrjh3B5YsvEwHv9gJxDsJi0gRGAJUCPL6JpiwMHjHStvhigW4j6YAYDBgwfjiy++CN7WahNi2NSG6l5M00x66DUyKl1ebDhUgfUHK2ouLpqQeh47im2xHnZMVLoCwQyEgMevQqrpmu3xBwrYCQCWJB1srsDMTN3ZrtQkXc05fMHgEAAWrT2AKncgefiY3Q0hBDSyBI0W8CsCe8qqMHfZNsyfWdBgQJOIS1SNbcmVJAmyLCFZr8HI7mmQZQn56SaYDVr4FRVeRYVQA/kyRp0GXVMMMOk0KHd4OuUSKFE0JURUoNVqkZ2d3axjPR4PPB5P8Lbd3rmSPjuDcHkLDo8fx6sC22BRU9LfZNCH1PN4c+PhWA895kptbmhq4gVZlgAp0NZAVYFH3t+B60d3x1f7T+BAuQPVHgUunxK8+NYGh5MHZ2NXiQ1Or4KKal9tT0sINdBgUKuRoAoBm8vf4cr/t3RLbrpJj2S9BiaDHhBScCeTUR/op+XyKaxHQhQFCdHOYP/+/cjNzUWvXr1w/fXX4/Dhhi9K8+fPh9VqDf7k5+e340ipPZyZtyCEwPGqQIKlTg7sEPEqKiAkZFsMcHgU/H3NAfxw6GSshx5TtSsjSk1TwNrlpQyzAWkmLU44PPh/n+3DrhIbJAlw+fxQVBV+RYXT44ckSdhdWoX/99lenHL64PWrqLvZXRWBvBxVDSylJOk1EZf/j1ctbc9QG/xUOv0w6mSkGHVI0msgQeqwpf6JYiHug5nRo0fj9ddfx8qVK7Fo0SIcOnQI48ePR1VVVdjj586dC5vNFvw5cuRIO4+Y2tqZeQtunwqPX63ZKSKdbrioqsHdOnvLqmBz+Zo4c8dWt52SQCD4cPsFSm1uHD3lhsOjwO1XodPIsLl8UASg08jQaWUIADaXD5kpOlS5/YH2CTXnkiQEqw4LBLpxAwJJOk29bslnzqoZdRrIsgSjThMMPBetPRDXNYFqt+QOzEmB0+NHucMDp8ePgTkp9Sr6JlJvKqJEFvfLTFOnTg3+d0FBAUaPHo0ePXrg7bffxi233FLveIPBAIPB0J5DpHZ2Zt6CXz1duwNA8L+1Nb2HdHJgGcrjj98LZKwp4vRrc8zugSwBGo0EAQTzPDw+BXaXEjwuGBsJADUBTc0qH3SaQBfvlpb/b8028fbUki25rEdC1PbiPpg5U2pqKvr164eioqJYD4Vi5My8Ba0sB2t3AAKKKmDUaaDVAOVVbpxweKHE8Tf9eCNwupll7atae4mu9vprY5dAMCICCcTSGS+vxaiDzeWvV9K9IzXoa8mWXNYjIWpbcb/MdCaHw4EDBw4gJycn1kOhGDlz6l4gcHH0Kyp8/tNrKXvLHDhm9zCQiZLapSalZgeUECIY5NSGPRIAjQR4/GrYJZS6s2rhdOQGfbXBz/n9umJoNysDGaIoivtg5sEHH8TatWvx008/Yd26dZgxYwY0Gg2uvfbaWA+NYqhu3oLLq0AjS8E8EL8q4PD4Q5JTtbIEky7u3+5xqzZQqRsX6mQJeq2Mmk1RwQ8TjSxjSJ4lbEfo2lm1U04fhAgNMpkQS0SRivtP96NHj+Laa69F//79cdVVV6FLly747rvv0LVr11gPjWJsbJ8MvHBNIaYMyYZeK0MNFiY7TaeRcOWIPPzjpnNg7YDf9ttT3ddWI0uoTUHSaQJJ17IsITPFgMcuH4R//mp02FwQJsQSUVuI+5yZpUuXxnoIFIcOHHdg8TeH8N7mYrh8Sr3Hz+piwswR3XD7+F4w6jVYu+84qtz+GIy0YwnkygAZZj2qPf5g40RZkiBLEu65qC9+eW6PRs/BhFgiira4D2aIaqmqwNr9x/Ha14fwTZhaJBpJwuQhWbhlXE+M6J4WslsmNUkHp5fBTKSkmh+NJnArxaBDV7MBbl+gJYIsSbC7fchPNzXrfEyIJaJoYjBDca/a48d/vj+Cf67/CT+drN9h2JKkxbXndMeNY89CbmpS2HOoQoB5wJGREGiICACqCiTpZRh1geq3gaaVGrh8CvQauUWJu2zQR0TRwmCG4tZPJ6rx2jeHsHxLMRye+rMqvTKScfN5Z+HKkfn1OkGf6cejnbMvU7TotTL8amDbe4bZ0GQZfyKi9sRghuKKqqpYs+843lj3M77ef7zebIoEYFzfDNwyrifO79e1XuG1hkg1FWs5OdNysgSY9FrkphpRXuWBw6NAq5Fh0MjwKCoqnT4m7hJRTDGYobhQ6fTi3U1H8dbGwzhwvLre4ya9Br8oyMUt43qif3ZKi88/vHsq9FoZHn/4+iadVd06MXVpJMBi1KBHRgomD87CuD5dMTjXgu8OnmTiLhHFHQYzFDN+RcXBEw78+7vDWLG1JGzvpLzUJPzX2d1w/eju6JpijPi5huZZ0S0tKWyg1JHVnSdJNmhg1GqQYdajvMoNIYBkow4aSYIiVFS5/TBqNZg1ohvO6ZWOjGRDvaRcJu4SUTxiMEPtSlUFqr1+fHfwJN7aeARr9x0PW6F3ZPdUXHVOPqYOyYElSdfq55VlCVeOzMdTK/e0+lzxQisHclmc3vCzTbIEpCZp0b2LOWR2RZYlrCs6EZxh8aiBqrsF3VKbNcPCxF0iijcMZqhduLwKTjk9+HR7Gd7dfBS7S+t3PTdoZVwyKAv/NTIfw3ukIsWgbXZOTHOM65OBp6J2trbXWI6PQSvhwUv6Y3CuFU9+sgt7yhzBoFCnkZCfbsKVI/Mxrk9G2JkTzrAQUUfCYIbajNevwuHx42iFE8u3FuODH0tw0lG/gWBmigHTh+fi8mG56J6eDEtSdIOYWvG200aSakpwS4BODrQFSE3WY9KATMws7AZJlrDuwAms2nkMZZVOuBUBGUD3Lsl48JJ+GNc3UAX7g7vHY3uxDVuOVEISgfygoXlN9/7hDAsRdRQMZiiqlJq+SA6PH7uKbXhvczFW7zkGn1J/jmFonhUzR+RhfN8MZJgNsBh1bTozEA+zDsl6GT3STTh/QBZGn5WOtORAXZZKly/s7Miw/FTcMaF3ozMosixhWH4qhuWntvefQ0QUFxjMUKsJIeD0KnB4/Khy+/Ft0Qks23w0bG0XnUbCBf0zMXNEHvpnW2BN0sGapIOmHQKNdWGqBreH7BQDpo/Iw+heXcIm1TaFMyhERI1jMEMR8/gVVLn9qPb4Uen04tMdZVi+pRjH7J56x6Yn63HFsBz8oiAXXcwGpBi1SDPp2yWIAQKJx4vWHmiX56olA7jxvLPw6GWD4mJWiIioo2IwQy3iV9TgDIxPUXH4pBPLthTjs51lcIep4dI/KwUzR+RhYv+u0GlkmGuCGJ2mfRu27yyx40C5o12eS6+RMCjHigcnn85rISKitsNghpokhEC1V4HD7YfT64cqBL7/qQLLNhfj+59O1TteloDz+3XFjMI8DM61QJIkmA1apJr00GvbN4ipVeH0hu2uHU1DclMw5+L+6Jpi5M4gIqJ2xGCGGuT2nV5GUoWAy6tg1c7AUtKRU656x1uMWvyiIAfThueha4oBQKAMflqyDgZt472T2lq6SQ+5DXZISQByrQbMn1WACf0yo35+IiJqGoMZCuFTVDjcgd1IPiWwbFRqc+H9LSX4ZEcpqj31ZzfO6mLCrBHdMGlgJgy6QNCSpNcgzaSHURfbIKbW4FwL8tNNOFldf2t4S9067izkppmggdTsbdBERNR2GMwQVFXA4fXD4fbDXbMUI4TAj0dteG/zUaw/cDJsw8dze3XBrBF5KOyeGqwLY9BpkG7SN9nFur3JsoQHL+mHX762sVXnuW5UNzzyi8FRGhUREUUDg5lOzFkTwFR7FQgRiFa8fhWrdx/De1uKcbCBho9Th2RjemEe8lKTgvfrtTLSTHokG+L3LdXaZNzrzumGJ2cOi9JoiIgoWuL3ykNtwuNXgstIdXsiHa/y4IMfS/DRttIGGz7OKMzDlCFZMOlPv210GhmpJh1SjK3vnxSPNDJwVroJf7x8CMb3584kIqJ4xGCmE1BUAYfbjyqPD94ztk/vKrHjvc1H8dX+E+EbPvZIw6wReRjVMz0kgVYry0hN1kW9f1I8ee2/RyLTmsSdSUREcY7BTAdVdzu1y3d6GQkIJPl+te843ttcjD1lDTd8nDEiD2d1SQ55TCNLSE3St1n/pHhy0eDsWA+BiIiagcFMB3Pmduq6Kp1efLitFB9sLQm7qyczxYDphXm4dEg2LEmhy0ayJAVbD3CWgoiI4gmDmQ4g3Hbqug6UO5ps+DhrRB7O65NRr72AVCeIaa/WA0RERC3BYCZBqapAtTfQVsAdprKtogqsO3Cy0YaPFw7IxMzCPPTNSqn3uCRJSDFqkZqkg7adWw8QERG1BIOZBBNuO3VdDrcfn+woxftbSlBmd9d7vG7Dx/RkfdjniFX/JCIiokgwmEkAXn+guaPD7Ydfrb+MBACHTzqxfEsxVu0qg9vXdMPHcGLdP4mIiCgSDGbilKKKQADj8cPTQIPE5jR8nNC3K2aOON3wMZx46Z9EREQUCQYzcUQIAadXgcPjh7OBZSQAzW74eMWwXGRajA0+n1GnQXpy/PRPIiIiigSDmTjg9gUCmOozqvKeqamGjz0zkjGzMC+k4WM4eq2M9GR9SCVfIiKiRMWrWYz4lUAeTJU7/HbqWkIIbD1SiWVbihts+DimdxfMHJGHwvzURgvZ6TSBICae+ycRERG1VEJc1V588UU888wzKCsrw7Bhw/DCCy9g1KhRsR5Wi9Vup3Z4/HB5w+fB1PL4FKzeU45lDTR8TNZrMCVMw8dwOnr/pJYY38uKrw/W36oe7jgiIkoMcR/M/Oc//8GcOXPw0ksvYfTo0Vi4cCEmT56MvXv3IjMzM9bDaxaXV0GVxwenR6lXlfdMtQ0fP/yxBHa3v97j3dICDR8nD85qcplIK8uwmnSwGDt+64HmevmXozHofz5r1nFERJQY4j6Y+ctf/oLbbrsNN998MwDgpZdewscff4x//OMfePjhh2M8uoY1Zzt1XU01fDy7Rxpmhmn4GE5n6p/UUiaTDoX5Vmw50vDsTGG+FSYTZ7GIiBJFXAczXq8XmzZtwty5c4P3ybKMSZMmYf369WF/x+PxwOPxBG/b7fY2H2et5mynrqupho9GrYyLB2dhZmEeepzR8DEc9k9qnuWzx2HGi9+EDWgK861YPntcDEZFRESRiutg5sSJE1AUBVlZWSH3Z2VlYc+ePWF/Z/78+Zg3b157DA9A87dT19Xcho+XDc1uVp6LJEmwGAMF79g/qXmWzx4Hp9OHOe9tw+GKanRPT8ZfZhVwRoaIKAHFdTATiblz52LOnDnB23a7Hfn5+W3yXE6vH8erPI1up66rqNyBZRE2fAxHkiSYDVqkmdg/KRImkw4v/ffIWA+DiIhaKa6DmYyMDGg0Ghw7dizk/mPHjiE7Ozvs7xgMBhgMhvYYHnyKaDKQUVSBbw+cwLLNxdjWSMPHGYV56Bem4WND2D+JiIgoIK6DGb1ej5EjR2L16tWYPn06AEBVVaxevRp33313bAfXhCq3D59sL8P7W4txzO6p93hzGj6Gk2wIBDHsn0RERBQQ18EMAMyZMwc33ngjzj77bIwaNQoLFy5EdXV1cHdTvDl80ollW4rx2c4yuP1hGj5mp2BmYeMNH8NJ0muQZmLrASIiojPFfTBz9dVX4/jx43jsscdQVlaG4cOHY+XKlfWSgmNJFQIbD1Vg+ZaGGz6e3y/Q8HFQTsMNH8Mx6gJBTJKeQQwREVE4kmjO9psEZrfbYbVaYbPZYLFYonrukkoX3tp4GMu3FONoIw0fpw3PQ9eUluXxsH8SERF1Zi25fvNKGYEymxuvfH0Q//n+CBye+lV6m9vwMRydRkZash5m9k8iIiJqFl4xI3DC4cFr3xwKuU8CMLam4ePwJho+hsP+SURERJFhMBOBIXlWnHNWGr7/6RSS9RpMHZqN6cPzkNtEw8dwNLKEVJOe/ZOIiIgixGAmQvdc2Be7Su0Y16dLRHktGvl06wEGMURERJFjMBOhCf26Ylh+Kk466teQaYwsSbAk6ZDK/klERERRwWCmnbB/EhERUdtgMNPG2D+JiIiobTGYaUNmgxZpyeyfRERE1JYYzLSBZIMWqSYdDFpW7SUiImprDGaiiP2TiIiI2h+DmSgw6DRIZ/8kIiKimGAw0wp6jYxsq5H9k4iIiGKIV+FW4EwMERFR7HGbDRERESU0BjNERESU0BjMEBERUUJjMENEREQJjcEMERERJTQGM0RERJTQGMwQERFRQmMwQ0RERAmNwQwRERElNAYzRERElNAYzBAREVFCYzBDRERECY3BDBERESU0BjNERESU0BjMEBERUULTxnoAbU0IAQCw2+0xHgkRERE1V+11u/Y63pgOH8xUVVUBAPLz82M8EiIiImqpqqoqWK3WRo+RRHNCngSmqipKSkqQkpICSZJiPZyYs9vtyM/Px5EjR2CxWGI9nJjj6xGKr0d9fE1C8fUIxdcjVDRfDyEEqqqqkJubC1luPCumw8/MyLKMbt26xXoYccdisfAfXh18PULx9aiPr0kovh6h+HqEitbr0dSMTC0mABMREVFCYzBDRERECY3BTCdjMBjw+OOPw2AwxHoocYGvRyi+HvXxNQnF1yMUX49QsXo9OnwCMBEREXVsnJkhIiKihMZghoiIiBIagxkiIiJKaAxmiIiIKKExmOlgKioqcP3118NisSA1NRW33HILHA5Ho8ffc8896N+/P5KSktC9e3fce++9sNlsIcdJklTvZ+nSpW3950TkxRdfxFlnnQWj0YjRo0dj48aNjR7/zjvvYMCAATAajRg6dCg++eSTkMeFEHjssceQk5ODpKQkTJo0Cfv372/LPyGqWvJ6vPLKKxg/fjzS0tKQlpaGSZMm1Tv+pptuqvdemDJlSlv/GVHTktfj9ddfr/e3Go3GkGM60/tj4sSJYT8LLrvssuAxifz++Oqrr3D55ZcjNzcXkiTh/fffb/J31qxZgxEjRsBgMKBPnz54/fXX6x3T0s+keNLS12TZsmW4+OKL0bVrV1gsFowZMwarVq0KOeaPf/xjvffIgAEDWjdQQR3KlClTxLBhw8R3330nvv76a9GnTx9x7bXXNnj89u3bxcyZM8UHH3wgioqKxOrVq0Xfvn3FrFmzQo4DIBYvXixKS0uDPy6Xq63/nBZbunSp0Ov14h//+IfYuXOnuO2220Rqaqo4duxY2OO//fZbodFoxNNPPy127dolHnnkEaHT6cT27duDxyxYsEBYrVbx/vvvix9//FFcccUVomfPnnH595+ppa/HddddJ1588UWxZcsWsXv3bnHTTTcJq9Uqjh49GjzmxhtvFFOmTAl5L1RUVLTXn9QqLX09Fi9eLCwWS8jfWlZWFnJMZ3p/nDx5MuS12LFjh9BoNGLx4sXBYxL5/fHJJ5+IP/zhD2LZsmUCgFi+fHmjxx88eFCYTCYxZ84csWvXLvHCCy8IjUYjVq5cGTympa9xvGnpa/Kb3/xGPPXUU2Ljxo1i3759Yu7cuUKn04nNmzcHj3n88cfF4MGDQ94jx48fb9U4Gcx0ILt27RIAxPfffx+879NPPxWSJIni4uJmn+ftt98Wer1e+Hy+4H3NeRPHg1GjRonZs2cHbyuKInJzc8X8+fPDHn/VVVeJyy67LOS+0aNHizvuuEMIIYSqqiI7O1s888wzwccrKyuFwWAQb731Vhv8BdHV0tfjTH6/X6SkpIg33ngjeN+NN94opk2bFu2htouWvh6LFy8WVqu1wfN19vfHc889J1JSUoTD4Qjel8jvj7qa85n3u9/9TgwePDjkvquvvlpMnjw5eLu1r3E8ifQ6MGjQIDFv3rzg7ccff1wMGzYsegMTQnCZqQNZv349UlNTcfbZZwfvmzRpEmRZxoYNG5p9HpvNBovFAq02tHXX7NmzkZGRgVGjRuEf//hHs9qytyev14tNmzZh0qRJwftkWcakSZOwfv36sL+zfv36kOMBYPLkycHjDx06hLKyspBjrFYrRo8e3eA540Ukr8eZnE4nfD4f0tPTQ+5fs2YNMjMz0b9/f9x11104efJkVMfeFiJ9PRwOB3r06IH8/HxMmzYNO3fuDD7W2d8fr732Gq655hokJyeH3J+I749INPX5EY3XONGpqoqqqqp6nyH79+9Hbm4uevXqheuvvx6HDx9u1fMwmOlAysrKkJmZGXKfVqtFeno6ysrKmnWOEydO4E9/+hNuv/32kPv/53/+B2+//TY+//xzzJo1C7/+9a/xwgsvRG3s0XDixAkoioKsrKyQ+7Oyshr8+8vKyho9vvZ/W3LOeBHJ63Gmhx56CLm5uSEfxlOmTME///lPrF69Gk899RTWrl2LqVOnQlGUqI4/2iJ5Pfr3749//OMfWLFiBf79739DVVWMHTsWR48eBdC53x8bN27Ejh07cOutt4bcn6jvj0g09Plht9vhcrmi8m8w0T377LNwOBy46qqrgveNHj0ar7/+OlauXIlFixbh0KFDGD9+PKqqqiJ+ng7fNbsjePjhh/HUU081eszu3btb/Tx2ux2XXXYZBg0ahD/+8Y8hjz366KPB/y4sLER1dTWeeeYZ3Hvvva1+XopPCxYswNKlS7FmzZqQpNdrrrkm+N9Dhw5FQUEBevfujTVr1uCiiy6KxVDbzJgxYzBmzJjg7bFjx2LgwIF4+eWX8ac//SmGI4u91157DUOHDsWoUaNC7u9M7w9q3JIlSzBv3jysWLEi5Iv21KlTg/9dUFCA0aNHo0ePHnj77bdxyy23RPRcnJlJAA888AB2797d6E+vXr2QnZ2N8vLykN/1+/2oqKhAdnZ2o89RVVWFKVOmICUlBcuXL4dOp2v0+NGjR+Po0aPweDyt/vuiJSMjAxqNBseOHQu5/9ixYw3+/dnZ2Y0eX/u/LTlnvIjk9aj17LPPYsGCBfjss89QUFDQ6LG9evVCRkYGioqKWj3mttSa16OWTqdDYWFh8G/trO+P6upqLF26tFkXnkR5f0Sioc8Pi8WCpKSkqLznEtXSpUtx66234u233663FHem1NRU9OvXr1XvEQYzCaBr164YMGBAoz96vR5jxoxBZWUlNm3aFPzdL7/8EqqqYvTo0Q2e326345JLLoFer8cHH3xQb+tpOFu3bkVaWlpcNVfT6/UYOXIkVq9eHbxPVVWsXr065Nt1XWPGjAk5HgA+//zz4PE9e/ZEdnZ2yDF2ux0bNmxo8JzxIpLXAwCefvpp/OlPf8LKlStD8q8acvToUZw8eRI5OTlRGXdbifT1qEtRFGzfvj34t3bG9wcQKGfg8Xjwy1/+ssnnSZT3RySa+vyIxnsuEb311lu4+eab8dZbb4Vs22+Iw+HAgQMHWvceiWo6McXclClTRGFhodiwYYP45ptvRN++fUO2Zh89elT0799fbNiwQQghhM1mE6NHjxZDhw4VRUVFIVvl/H6/EEKIDz74QLzyyiti+/btYv/+/eLvf/+7MJlM4rHHHovJ39iYpUuXCoPBIF5//XWxa9cucfvtt4vU1NTgdtr//u//Fg8//HDw+G+//VZotVrx7LPPit27d4vHH3887Nbs1NRUsWLFCrFt2zYxbdq0hNp625LXY8GCBUKv14t333035L1QVVUlhBCiqqpKPPjgg2L9+vXi0KFD4osvvhAjRowQffv2FW63OyZ/Y0u09PWYN2+eWLVqlThw4IDYtGmTuOaaa4TRaBQ7d+4MHtOZ3h+1xo0bJ66++up69yf6+6Oqqkps2bJFbNmyRQAQf/nLX8SWLVvEzz//LIQQ4uGHHxb//d//HTy+dmv2b3/7W7F7927x4osvht2a3dhrHO9a+pq8+eabQqvVihdffDHkM6SysjJ4zAMPPCDWrFkjDh06JL799lsxadIkkZGRIcrLyyMeJ4OZDubkyZPi2muvFWazWVgsFnHzzTcHL0RCCHHo0CEBQPzf//2fEEKI//u//xMAwv4cOnRICBHY3j18+HBhNptFcnKyGDZsmHjppZeEoigx+Aub9sILL4ju3bsLvV4vRo0aJb777rvgY+eff7648cYbQ45/++23Rb9+/YRerxeDBw8WH3/8ccjjqqqKRx99VGRlZQmDwSAuuugisXfv3vb4U6KiJa9Hjx49wr4XHn/8cSGEEE6nU1xyySWia9euQqfTiR49eojbbrstYT6YhWjZ63HfffcFj83KyhKXXnppSL0MITrX+0MIIfbs2SMAiM8++6zeuRL9/dHQ52Hta3DjjTeK888/v97vDB8+XOj1etGrV6+Qmju1GnuN411LX5Pzzz+/0eOFCGxfz8nJEXq9XuTl5Ymrr75aFBUVtWqckhBxtr+WiIiIqAWYM0NEREQJjcEMERERJTQGM0RERJTQGMwQERFRQmMwQ0RERAmNwQwRERElNAYzRERElNAYzBAREVGLffXVV7j88suRm5sLSZLw/vvvt/gcQgg8++yz6NevHwwGA/Ly8vDnP/+5xedhMENEHdq3336LoUOHQqfTYfr06VizZg0kSUJlZWWshxZ01llnYeHChbEeBlGLVFdXY9iwYXjxxRcjPsdvfvMbvPrqq3j22WexZ88efPDBB/U6sTeHNuIREBElgDlz5mD48OH49NNPYTabYTKZUFpaCqvVGuuhESW0qVOnYurUqQ0+7vF48Ic//AFvvfUWKisrMWTIEDz11FOYOHEiAGD37t1YtGgRduzYgf79+wMING+NBGdmiKhDO3DgAC688EJ069YNqamp0Ov1yM7OhiRJYY9XFAWqqrbzKIk6nrvvvhvr16/H0qVLsW3bNvzXf/0XpkyZgv379wMAPvzwQ/Tq1QsfffQRevbsibPOOgu33norKioqWvxcDGaIOpmJEyfi3nvvxe9+9zukp6cjOzsbf/zjH4OPV1ZW4tZbb0XXrl1hsVhw4YUX4scffwQA2Gw2aDQa/PDDDwAAVVWRnp6Oc889N/j7//73v5Gfn9+ssRw9ehTXXnst0tPTkZycjLPPPhsbNmwIPr5o0SL07t0ber0e/fv3x7/+9a+Q35ckCa+++ipmzJgBk8mEvn374oMPPgAA/PTTT5AkCSdPnsSvfvUrSJKE119/vd4y0+uvv47U1FR88MEHGDRoEAwGAw4fPoyzzjoLTzzxBG644QaYzWb06NEDH3zwAY4fP45p06bBbDajoKAg+FrU+uabbzB+/HgkJSUhPz8f9957L6qrq4OPl5eX4/LLL0dSUhJ69uyJN998s1mvFVEiOXz4MBYvXox33nkH48ePR+/evfHggw9i3LhxWLx4MQDg4MGD+Pnnn/HOO+/gn//8J15//XVs2rQJV155ZcufsFVtKoko4Zx//vnCYrGIP/7xj2Lfvn3ijTfeEJIkBbsgT5o0SVx++eXi+++/F/v27RMPPPCA6NKlizh58qQQQogRI0aIZ555RgghxNatW0V6errQ6/XB7uy33nqruP7665scR1VVlejVq5cYP368+Prrr8X+/fvFf/7zH7Fu3TohhBDLli0TOp1OvPjii2Lv3r3i//2//yc0Go348ssvg+cAILp16yaWLFki9u/fL+69915hNpvFyZMnhd/vF6WlpcJisYiFCxeK0tJS4XQ6g12AT506JYQQYvHixUKn04mxY8eKb7/9VuzZs0dUV1eLHj16iPT0dPHSSy+Jffv2ibvuuktYLBYxZcoU8fbbb4u9e/eK6dOni4EDBwpVVYUQQhQVFYnk5GTx3HPPiX379olvv/1WFBYWiptuuik45qlTp4phw4aJ9evXix9++EGMHTtWJCUlieeee651/8cSxRAAsXz58uDtjz76SAAQycnJIT9arVZcddVVQgghbrvtNgEgpMv8pk2bBACxZ8+elj1/VP4KIkoY559/vhg3blzIfeecc4546KGHxNdffy0sFotwu90hj/fu3Vu8/PLLQggh5syZIy677DIhhBALFy4UV199tRg2bJj49NNPhRBC9OnTR/zv//5vk+N4+eWXRUpKSjBIOtPYsWPFbbfdFnLff/3Xf4lLL700eBuAeOSRR4K3HQ6HABAcixBCWK1WsXjx4uDtcMEMALF169aQ5+rRo4f45S9/GbxdWloqAIhHH300eN/69esFAFFaWiqEEOKWW24Rt99+e8h5vv76ayHLsnC5XGLv3r0CgNi4cWPw8d27dwsADGYooZ0ZzCxdulRoNBqxZ88esX///pCf2n8vjz32mNBqtSHncTqdAkDwy1VzMQGYqBMqKCgIuZ2Tk4Py8nL8+OOPcDgc6NKlS8jjLpcLBw4cAACcf/75eO2116AoCtauXYtLLrkE2dnZWLNmDQoKClBUVBRM8GvM1q1bUVhYiPT09LCP7969G7fffnvIfeeddx6ef/75Bv+W5ORkWCwWlJeXN/n8den1+nqvyZnnzsrKAgAMHTq03n3l5eXIzs7Gjz/+iG3btoUsHQkhoKoqDh06hH379kGr1WLkyJHBxwcMGIDU1NQWjZco3hUWFkJRFJSXl2P8+PFhjznvvPPg9/tx4MAB9O7dGwCwb98+AECPHj1a9HwMZog6IZ1OF3JbkiSoqgqHw4GcnBysWbOm3u/UXnAnTJiAqqoqbN68GV999RWefPJJZGdnY8GCBRg2bBhyc3PRt2/fJseQlJQUjT+lwb+lJZKSksImBNc9d+3j4e6rfT6Hw4E77rgD9957b71zde/ePfhBTdQROBwOFBUVBW8fOnQIW7duRXp6Ovr164frr78eN9xwA/7f//t/KCwsxPHjx7F69WoUFBTgsssuw6RJkzBixAj86le/wsKFC6GqKmbPno2LL74Y/fr1a9FYmABMREEjRoxAWVkZtFot+vTpE/KTkZEBIBDUFBQU4G9/+xt0Oh0GDBiACRMmYMuWLfjoo49w/vnnN+u5CgoKsHXr1gZ3LgwcOBDffvttyH3ffvstBg0a1Lo/sg2NGDECu3btqvfa9enTB3q9HgMGDIDf78emTZuCv7N37964qnlD1Fw//PADCgsLUVhYCCBQBqGwsBCPPfYYAGDx4sW44YYb8MADD6B///6YPn06vv/+e3Tv3h0AIMsyPvzwQ2RkZGDChAm47LLLMHDgQCxdurTFY+HMDBEFTZo0CWPGjMH06dPx9NNPo1+/figpKcHHH3+MGTNm4OyzzwYQ2BH1wgsvBHcdpKenY+DAgfjPf/7T7AJa1157LZ588klMnz4d8+fPR05ODrZs2YLc3FyMGTMGv/3tb3HVVVehsLAQkyZNwocffohly5bhiy++aLO/v7UeeughnHvuubj77rtx6623Ijk5Gbt27cLnn3+Ov/3tb+jfvz+mTJmCO+64A4sWLYJWq8V9990XtVkqovY0ceJEBNJlwtPpdJg3bx7mzZvX4DG5ubl47733Wj0WzswQUZAkSfjkk08wYcIE3HzzzejXrx+uueYa/Pzzz8H8ECCQN6MoSkhuzMSJE+vd1xi9Xo/PPvsMmZmZuPTSSzF06FAsWLAAGo0GADB9+nQ8//zzePbZZzF48GC8/PLLWLx4cbPPHwsFBQVYu3Yt9u3bh/Hjxwe/pebm5gaPWbx4MXJzc3H++edj5syZuP3225GZmRnDURMlPkk0FlYRERERxTnOzBAREVFCYzBDRG3iySefhNlsDvvTWD8XIqKW4jITEbWJioqKBncqJSUlIS8vr51HREQdFYMZIiIiSmhcZiIiIqKExmCGiIiIEhqDGSIiIkpoDGaIiIgooTGYISIiooTGYIaIiIgSGoMZIiIiSmj/HyLI9te1eWk1AAAAAElFTkSuQmCC",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -622,7 +712,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 15,
       "metadata": {
         "id": "-S1A9E3WGaYH"
       },
@@ -633,13 +723,13 @@
               "<Axes: xlabel='new_confirmed', ylabel='search_trends_bruise'>"
             ]
           },
-          "execution_count": 16,
+          "execution_count": 15,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
+            "image/png": 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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -692,7 +782,8 @@
       "provenance": []
     },
     "kernelspec": {
-      "display_name": "Python 3",
+      "display_name": "venv",
+      "language": "python",
       "name": "python3"
     },
     "language_info": {
@@ -705,7 +796,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.1"
+      "version": "3.12.6"
     }
   },
   "nbformat": 4,

From 91e9ade6088cf5e547fd370ec1f5ec37c0c6b503 Mon Sep 17 00:00:00 2001
From: Tim Swena <swast@google.com>
Date: Fri, 14 Feb 2025 17:27:29 -0600
Subject: [PATCH 3/6] match by key

---
 bigframes/core/groupby/__init__.py            |  24 +-
 .../bq_dataframes_covid_line_graphs.ipynb     | 521 +++++++++++++++++-
 2 files changed, 517 insertions(+), 28 deletions(-)

diff --git a/bigframes/core/groupby/__init__.py b/bigframes/core/groupby/__init__.py
index db645a7c9e..c5c4e2c016 100644
--- a/bigframes/core/groupby/__init__.py
+++ b/bigframes/core/groupby/__init__.py
@@ -144,14 +144,17 @@ def head(self, n: int = 5) -> df.DataFrame:
         )
 
     def __iter__(self) -> Iterable[Tuple[blocks.Label, pd.DataFrame]]:
-        # TODO: cache original block, clustered by column ids
+        # Cache original block, clustered by column ids.
+        # To force block.cached() to cluster by our by_col_ids,
+        # we set those columns as the index. This also makes filtering
+        # by our groupby key a bit easier with respect to fewer
+        # cases to worry about (e.g. MultiIndex).
+        original_index_labels = self._block._index_labels
+        by_col_labels = self._block._get_labels_for_columns(self._by_col_ids).to_list()
         block = self._block.set_index(
             self._by_col_ids,
-            # TODO: do we need to keep the original index?
             drop=False,
-            index_labels=self._block._get_labels_for_columns(
-                self._by_col_ids
-            ).to_list(),
+            index_labels=by_col_labels,
         )
         block.cached(force=True)
 
@@ -161,14 +164,9 @@ def __iter__(self) -> Iterable[Tuple[blocks.Label, pd.DataFrame]]:
         )
         for batch in keys_block.to_pandas_batches():
             for key in batch.index:
-                # group_block = block
-                # for col in self._by_col_ids:  # TODO: can't loop through key if only one by_col_id.
-
-                #
-                #  = block.project_expr(bigframes.core.expression.const(key, dtype=self._block._column_type(self._by_col_ids))
-                #     ops.eq_op( ex.const(key)
-                # )
-                yield key, batch  # TODO: filter clustered block by row
+                yield key, df.DataFrame(block).loc[key].set_index(
+                    original_index_labels, drop=False
+                )
 
     def size(self) -> typing.Union[df.DataFrame, series.Series]:
         agg_block, _ = self._block.aggregate_size(
diff --git a/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb b/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb
index 6dccb39e5f..d8ee2d491c 100644
--- a/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb
+++ b/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb
@@ -272,7 +272,7 @@
         {
           "data": {
             "text/html": [
-              "Query job d9beb87c-89b3-44c6-9e99-082df80a4e95 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:d9beb87c-89b3-44c6-9e99-082df80a4e95&page=queryresults\">Open Job</a>"
+              "Query job 173629b3-e364-4a4c-bad2-859aa16534ff is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:173629b3-e364-4a4c-bad2-859aa16534ff&page=queryresults\">Open Job</a>"
             ],
             "text/plain": [
               "<IPython.core.display.HTML object>"
@@ -317,13 +317,95 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 7,
       "metadata": {
         "id": "IaoUf57ZwrJ8"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "Query job 994fc4c0-dde9-4d41-b3d4-c500a4185567 is DONE. 372.9 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:994fc4c0-dde9-4d41-b3d4-c500a4185567&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>date</th>\n",
+              "      <th>new_confirmed</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2020-10-08</td>\n",
+              "      <td>189</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2020-12-20</td>\n",
+              "      <td>148</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2021-01-16</td>\n",
+              "      <td>2543</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2020-08-15</td>\n",
+              "      <td>933</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2020-05-30</td>\n",
+              "      <td>790</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         date  new_confirmed\n",
+              "0  2020-10-08            189\n",
+              "1  2020-12-20            148\n",
+              "2  2021-01-16           2543\n",
+              "3  2020-08-15            933\n",
+              "4  2020-05-30            790"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
-        "usa_data = usa_data[[\"date\", \"new_confirmed\"]]"
+        "usa_data = usa_data[[\"date\", \"new_confirmed\"]]\n",
+        "usa_data.peek()"
       ]
     },
     {
@@ -347,13 +429,239 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 9,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/html": [
-              "Query job 33038dc8-ab4d-4940-a252-267215241412 is DONE. 372.9 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:33038dc8-ab4d-4940-a252-267215241412&page=queryresults\">Open Job</a>"
+              "Query job 7c47826d-dda8-4bbd-a659-2efd70978d16 is DONE. 26.0 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:7c47826d-dda8-4bbd-a659-2efd70978d16&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 44db01eb-668c-4587-b65c-592e52f6b10e is DONE. 26.0 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:44db01eb-668c-4587-b65c-592e52f6b10e&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 34600696-c4a4-4b81-a1a0-767209329490 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:34600696-c4a4-4b81-a1a0-767209329490&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job e76587b6-7635-4c3c-bd89-cf9ab1dd02c7 is DONE. 26.0 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:e76587b6-7635-4c3c-bd89-cf9ab1dd02c7&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 550dd9db-0345-425a-95a4-0f52bca7f279 is DONE. 26.0 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:550dd9db-0345-425a-95a4-0f52bca7f279&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job ca394bd9-988c-4068-8d82-bf5a31e1c557 is DONE. 26.0 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:ca394bd9-988c-4068-8d82-bf5a31e1c557&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 2e7e68f4-3a75-4d99-9b25-0d09e2aa4663 is DONE. 372.9 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:2e7e68f4-3a75-4d99-9b25-0d09e2aa4663&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 326d4aa6-f932-494d-a2fc-2b022038892d is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:326d4aa6-f932-494d-a2fc-2b022038892d&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 4bc64f59-c621-431c-9478-264d9366227c is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:4bc64f59-c621-431c-9478-264d9366227c&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 751bb678-2048-4d5e-9c67-dddab4ad326e is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:751bb678-2048-4d5e-9c67-dddab4ad326e&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 3d737d99-ee9e-4cd2-a5d1-1b6cafcd4fcd is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:3d737d99-ee9e-4cd2-a5d1-1b6cafcd4fcd&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 2eff18e2-1829-4c16-985c-e2692c7159f7 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:2eff18e2-1829-4c16-985c-e2692c7159f7&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job be5bdb37-58c6-4138-99f9-22e5636de959 is DONE. 26.0 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:be5bdb37-58c6-4138-99f9-22e5636de959&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 470f5b46-59c4-4db7-9244-5d6afb930b4b is DONE. 72.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:470f5b46-59c4-4db7-9244-5d6afb930b4b&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2020-01-01\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 78f73d75-cc26-4481-b51e-55a4f22c2a1e is DONE. 46.4 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:78f73d75-cc26-4481-b51e-55a4f22c2a1e&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 2ce6dc96-f036-46e2-a5d7-4b655f3d1b88 is DONE. 31 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:2ce6dc96-f036-46e2-a5d7-4b655f3d1b88&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "new_confirmed    0\n",
+            "dtype: Int64\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job d156d38c-81cc-4e14-86b1-e37d38191c6a is DONE. 72.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:d156d38c-81cc-4e14-86b1-e37d38191c6a&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2020-01-02\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 0b3b0a9f-a3f6-4cd1-8c13-ee74d3acb67d is DONE. 46.4 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:0b3b0a9f-a3f6-4cd1-8c13-ee74d3acb67d&page=queryresults\">Open Job</a>"
             ],
             "text/plain": [
               "<IPython.core.display.HTML object>"
@@ -365,7 +673,7 @@
         {
           "data": {
             "text/html": [
-              "Query job 68f3e706-1054-49a7-8b57-7d546b621ebf is DONE. 26.0 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:68f3e706-1054-49a7-8b57-7d546b621ebf&page=queryresults\">Open Job</a>"
+              "Query job cfc7c265-50bf-4867-8ec7-3c910f82fb4e is DONE. 31 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:cfc7c265-50bf-4867-8ec7-3c910f82fb4e&page=queryresults\">Open Job</a>"
             ],
             "text/plain": [
               "<IPython.core.display.HTML object>"
@@ -378,20 +686,203 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "2020-01-01\n",
-            "Empty DataFrame\n",
-            "Columns: []\n",
-            "Index: [2020-01-01, 2020-01-02, 2020-01-03, 2020-01-04, 2020-01-05, 2020-01-06, 2020-01-07, 2020-01-08, 2020-01-09, 2020-01-10, 2020-01-11, 2020-01-12, 2020-01-13, 2020-01-14, 2020-01-15, 2020-01-16, 2020-01-17, 2020-01-18, 2020-01-19, 2020-01-20, 2020-01-21, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26, 2020-01-27, 2020-01-28, 2020-01-29, 2020-01-30, 2020-01-31, 2020-02-01, 2020-02-02, 2020-02-03, 2020-02-04, 2020-02-05, 2020-02-06, 2020-02-07, 2020-02-08, 2020-02-09, 2020-02-10, 2020-02-11, 2020-02-12, 2020-02-13, 2020-02-14, 2020-02-15, 2020-02-16, 2020-02-17, 2020-02-18, 2020-02-19, 2020-02-20, 2020-02-21, 2020-02-22, 2020-02-23, 2020-02-24, 2020-02-25, 2020-02-26, 2020-02-27, 2020-02-28, 2020-02-29, 2020-03-01, 2020-03-02, 2020-03-03, 2020-03-04, 2020-03-05, 2020-03-06, 2020-03-07, 2020-03-08, 2020-03-09, 2020-03-10, 2020-03-11, 2020-03-12, 2020-03-13, 2020-03-14, 2020-03-15, 2020-03-16, 2020-03-17, 2020-03-18, 2020-03-19, 2020-03-20, 2020-03-21, 2020-03-22, 2020-03-23, 2020-03-24, 2020-03-25, 2020-03-26, 2020-03-27, 2020-03-28, 2020-03-29, 2020-03-30, 2020-03-31, 2020-04-01, 2020-04-02, 2020-04-03, 2020-04-04, 2020-04-05, 2020-04-06, 2020-04-07, 2020-04-08, 2020-04-09, ...]\n",
-            "\n",
-            "[991 rows x 0 columns]\n"
+            "new_confirmed    0\n",
+            "dtype: Int64\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job a00fc9c5-828b-4467-9daa-f415257722bd is DONE. 72.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:a00fc9c5-828b-4467-9daa-f415257722bd&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2020-01-03\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job b206e8dd-e08f-40ea-942f-fd3f51a934e6 is DONE. 46.4 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:b206e8dd-e08f-40ea-942f-fd3f51a934e6&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 1368108e-acfb-4e28-b513-0cc084c13cf7 is DONE. 31 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:1368108e-acfb-4e28-b513-0cc084c13cf7&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "new_confirmed    0\n",
+            "dtype: Int64\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 7ffd7934-2f13-4640-a676-a1e3ff63d60a is DONE. 72.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:7ffd7934-2f13-4640-a676-a1e3ff63d60a&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2020-01-04\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 525249e5-9591-43e2-9fe4-ccc40866558c is DONE. 46.4 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:525249e5-9591-43e2-9fe4-ccc40866558c&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 7cea5258-e7f7-4bdd-805c-0ca26da5ae52 is DONE. 31 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:7cea5258-e7f7-4bdd-805c-0ca26da5ae52&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "new_confirmed    0\n",
+            "dtype: Int64\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job adc51525-6973-4b13-a515-1b4cd011bb4f is DONE. 72.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:adc51525-6973-4b13-a515-1b4cd011bb4f&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2020-01-05\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "Query job 1e3ef3bb-c1dd-4c37-8921-42e4743f486a is RUNNING. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:1e3ef3bb-c1dd-4c37-8921-42e4743f486a&page=queryresults\">Open Job</a>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requested cancellation for Query job 1e3ef3bb-c1dd-4c37-8921-42e4743f486a in location US...\n"
+          ]
+        },
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "Cell \u001b[0;32mIn[9], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, group \u001b[38;5;129;01min\u001b[39;00m usa_data\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(key)\n\u001b[0;32m----> 3\u001b[0m     \u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/log_adapter.py:147\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    144\u001b[0m _call_stack\u001b[38;5;241m.\u001b[39mappend(full_method_name)\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mNotImplementedError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;66;03m# Log method parameters that are implemented in pandas but either missing (TypeError)\u001b[39;00m\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;66;03m# or not fully supported (NotImplementedError) in BigFrames.\u001b[39;00m\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;66;03m# Logging is currently supported only when we can access the bqclient through\u001b[39;00m\n\u001b[1;32m    152\u001b[0m     \u001b[38;5;66;03m# self._block.expr.session.bqclient. Also, to avoid generating multiple queries\u001b[39;00m\n\u001b[1;32m    153\u001b[0m     \u001b[38;5;66;03m# because of internal calls, we log only when the method is directly invoked.\u001b[39;00m\n\u001b[1;32m    154\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_block\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(_call_stack) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/series.py:345\u001b[0m, in \u001b[0;36mSeries.__repr__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m opts\u001b[38;5;241m.\u001b[39mrepr_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeferred\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    343\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m formatter\u001b[38;5;241m.\u001b[39mrepr_query_job(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_dry_run())\n\u001b[0;32m--> 345\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cached\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    346\u001b[0m pandas_df, _, query_job \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_block\u001b[38;5;241m.\u001b[39mretrieve_repr_request_results(max_results)\n\u001b[1;32m    347\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_internal_query_job(query_job)\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/log_adapter.py:147\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    144\u001b[0m _call_stack\u001b[38;5;241m.\u001b[39mappend(full_method_name)\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mNotImplementedError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;66;03m# Log method parameters that are implemented in pandas but either missing (TypeError)\u001b[39;00m\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;66;03m# or not fully supported (NotImplementedError) in BigFrames.\u001b[39;00m\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;66;03m# Logging is currently supported only when we can access the bqclient through\u001b[39;00m\n\u001b[1;32m    152\u001b[0m     \u001b[38;5;66;03m# self._block.expr.session.bqclient. Also, to avoid generating multiple queries\u001b[39;00m\n\u001b[1;32m    153\u001b[0m     \u001b[38;5;66;03m# because of internal calls, we log only when the method is directly invoked.\u001b[39;00m\n\u001b[1;32m    154\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_block\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(_call_stack) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/series.py:2071\u001b[0m, in \u001b[0;36mSeries._cached\u001b[0;34m(self, force, session_aware)\u001b[0m\n\u001b[1;32m   2070\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_cached\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m, force: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m, session_aware: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series:\n\u001b[0;32m-> 2071\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_block\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcached\u001b[49m\u001b[43m(\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msession_aware\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msession_aware\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2072\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/blocks.py:2440\u001b[0m, in \u001b[0;36mBlock.cached\u001b[0;34m(self, force, session_aware)\u001b[0m\n\u001b[1;32m   2438\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Write the block to a session table.\"\"\"\u001b[39;00m\n\u001b[1;32m   2439\u001b[0m \u001b[38;5;66;03m# use a heuristic for whether something needs to be cached\u001b[39;00m\n\u001b[0;32m-> 2440\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_executor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2441\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2442\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2443\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_session\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msession_aware\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2444\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcluster_cols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2445\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:459\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor.cached\u001b[0;34m(self, array_value, force, use_session, cluster_cols)\u001b[0m\n\u001b[1;32m    457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    458\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_session:\n\u001b[0;32m--> 459\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache_with_session_awareness\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    460\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    461\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache_with_cluster_cols(array_value, cluster_cols\u001b[38;5;241m=\u001b[39mcluster_cols)\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:584\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor._cache_with_session_awareness\u001b[0;34m(self, array_value)\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache_with_offsets(bigframes\u001b[38;5;241m.\u001b[39mcore\u001b[38;5;241m.\u001b[39mArrayValue(target))\n\u001b[1;32m    583\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 584\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache_with_cluster_cols\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbigframes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mArrayValue\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:537\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor._cache_with_cluster_cols\u001b[0;34m(self, array_value, cluster_cols)\u001b[0m\n\u001b[1;32m    532\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Executes the query and uses the resulting table to rewrite future executions.\"\"\"\u001b[39;00m\n\u001b[1;32m    534\u001b[0m sql, schema, ordering_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompiler\u001b[38;5;241m.\u001b[39mcompile_raw(\n\u001b[1;32m    535\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreplace_cached_subtrees(array_value\u001b[38;5;241m.\u001b[39mnode)\n\u001b[1;32m    536\u001b[0m )\n\u001b[0;32m--> 537\u001b[0m tmp_table \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sql_as_cached_temp_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    538\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    539\u001b[0m \u001b[43m    \u001b[49m\u001b[43mschema\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    540\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcluster_cols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbq_io\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect_cluster_cols\u001b[49m\u001b[43m(\u001b[49m\u001b[43mschema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcluster_cols\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    541\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    542\u001b[0m cached_replacement \u001b[38;5;241m=\u001b[39m array_value\u001b[38;5;241m.\u001b[39mas_cached(\n\u001b[1;32m    543\u001b[0m     cache_table\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbqclient\u001b[38;5;241m.\u001b[39mget_table(tmp_table),\n\u001b[1;32m    544\u001b[0m     ordering\u001b[38;5;241m=\u001b[39mordering_info,\n\u001b[1;32m    545\u001b[0m )\u001b[38;5;241m.\u001b[39mnode\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_executions[array_value\u001b[38;5;241m.\u001b[39mnode] \u001b[38;5;241m=\u001b[39m cached_replacement\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:631\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor._sql_as_cached_temp_table\u001b[0;34m(self, sql, schema, cluster_cols)\u001b[0m\n\u001b[1;32m    626\u001b[0m job_config \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m    627\u001b[0m     bigquery\u001b[38;5;241m.\u001b[39mQueryJobConfig,\n\u001b[1;32m    628\u001b[0m     bigquery\u001b[38;5;241m.\u001b[39mQueryJobConfig\u001b[38;5;241m.\u001b[39mfrom_api_repr({}),\n\u001b[1;32m    629\u001b[0m )\n\u001b[1;32m    630\u001b[0m job_config\u001b[38;5;241m.\u001b[39mdestination \u001b[38;5;241m=\u001b[39m temp_table\n\u001b[0;32m--> 631\u001b[0m _, query_job \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_execute_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    632\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    633\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjob_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjob_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[43m    \u001b[49m\u001b[43mapi_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcached\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    635\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    636\u001b[0m query_job\u001b[38;5;241m.\u001b[39mdestination\n\u001b[1;32m    637\u001b[0m query_job\u001b[38;5;241m.\u001b[39mresult()\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:497\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor._run_execute_query\u001b[0;34m(self, sql, job_config, api_name, page_size, max_results)\u001b[0m\n\u001b[1;32m    495\u001b[0m bq_io\u001b[38;5;241m.\u001b[39madd_and_trim_labels(job_config, api_name\u001b[38;5;241m=\u001b[39mapi_name)\n\u001b[1;32m    496\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 497\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbq_io\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart_query_with_client\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbqclient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    499\u001b[0m \u001b[43m        \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjob_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjob_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mapi_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_results\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpage_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpage_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetrics\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    505\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    507\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m google\u001b[38;5;241m.\u001b[39mapi_core\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mBadRequest \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    508\u001b[0m     \u001b[38;5;66;03m# Unfortunately, this error type does not have a separate error code or exception type\u001b[39;00m\n\u001b[1;32m    509\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResources exceeded during query execution\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m e\u001b[38;5;241m.\u001b[39mmessage:\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/_io/bigquery/__init__.py:253\u001b[0m, in \u001b[0;36mstart_query_with_client\u001b[0;34m(bq_client, sql, job_config, location, project, max_results, page_size, timeout, api_name, metrics)\u001b[0m\n\u001b[1;32m    251\u001b[0m opts \u001b[38;5;241m=\u001b[39m bigframes\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mdisplay\n\u001b[1;32m    252\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m opts\u001b[38;5;241m.\u001b[39mprogress_bar \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m query_job\u001b[38;5;241m.\u001b[39mconfiguration\u001b[38;5;241m.\u001b[39mdry_run:\n\u001b[0;32m--> 253\u001b[0m     results_iterator \u001b[38;5;241m=\u001b[39m \u001b[43mformatting_helpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_for_query_job\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_job\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_results\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopts\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpage_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpage_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    260\u001b[0m     results_iterator \u001b[38;5;241m=\u001b[39m query_job\u001b[38;5;241m.\u001b[39mresult(\n\u001b[1;32m    261\u001b[0m         max_results\u001b[38;5;241m=\u001b[39mmax_results, page_size\u001b[38;5;241m=\u001b[39mpage_size\n\u001b[1;32m    262\u001b[0m     )\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/formatting_helpers.py:139\u001b[0m, in \u001b[0;36mwait_for_query_job\u001b[0;34m(query_job, max_results, page_size, progress_bar)\u001b[0m\n\u001b[1;32m    137\u001b[0m loading_bar \u001b[38;5;241m=\u001b[39m display\u001b[38;5;241m.\u001b[39mHTML(get_query_job_loading_html(query_job))\n\u001b[1;32m    138\u001b[0m display\u001b[38;5;241m.\u001b[39mdisplay(loading_bar, display_id\u001b[38;5;241m=\u001b[39mdisplay_id)\n\u001b[0;32m--> 139\u001b[0m query_result \u001b[38;5;241m=\u001b[39m \u001b[43mquery_job\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_results\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpage_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpage_size\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    142\u001b[0m query_job\u001b[38;5;241m.\u001b[39mreload()\n\u001b[1;32m    143\u001b[0m display\u001b[38;5;241m.\u001b[39mupdate_display(\n\u001b[1;32m    144\u001b[0m     display\u001b[38;5;241m.\u001b[39mHTML(get_query_job_loading_html(query_job)),\n\u001b[1;32m    145\u001b[0m     display_id\u001b[38;5;241m=\u001b[39mdisplay_id,\n\u001b[1;32m    146\u001b[0m )\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/job/query.py:1681\u001b[0m, in \u001b[0;36mQueryJob.result\u001b[0;34m(self, page_size, max_results, retry, timeout, start_index, job_retry)\u001b[0m\n\u001b[1;32m   1676\u001b[0m     remaining_timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remaining_timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1679\u001b[0m     \u001b[38;5;66;03m# Since is_job_done() calls jobs.getQueryResults, which is a\u001b[39;00m\n\u001b[1;32m   1680\u001b[0m     \u001b[38;5;66;03m# long-running API, don't delay the next request at all.\u001b[39;00m\n\u001b[0;32m-> 1681\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mis_job_done\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1682\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m   1683\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1684\u001b[0m     \u001b[38;5;66;03m# Use a monotonic clock since we don't actually care about\u001b[39;00m\n\u001b[1;32m   1685\u001b[0m     \u001b[38;5;66;03m# daylight savings or similar, just the elapsed time.\u001b[39;00m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:293\u001b[0m, in \u001b[0;36mRetry.__call__.<locals>.retry_wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    289\u001b[0m target \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    290\u001b[0m sleep_generator \u001b[38;5;241m=\u001b[39m exponential_sleep_generator(\n\u001b[1;32m    291\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initial, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maximum, multiplier\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multiplier\n\u001b[1;32m    292\u001b[0m )\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mretry_target\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predicate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43msleep_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m    \u001b[49m\u001b[43mon_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:144\u001b[0m, in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sleep \u001b[38;5;129;01min\u001b[39;00m sleep_generator:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 144\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mtarget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    145\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39misawaitable(result):\n\u001b[1;32m    146\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(_ASYNC_RETRY_WARNING)\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/job/query.py:1650\u001b[0m, in \u001b[0;36mQueryJob.result.<locals>.is_job_done\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m   1646\u001b[0m \u001b[38;5;66;03m# Call jobs.getQueryResults with max results set to 0 just to\u001b[39;00m\n\u001b[1;32m   1647\u001b[0m \u001b[38;5;66;03m# wait for the query to finish. Unlike most methods,\u001b[39;00m\n\u001b[1;32m   1648\u001b[0m \u001b[38;5;66;03m# jobs.getQueryResults hangs as long as it can to ensure we\u001b[39;00m\n\u001b[1;32m   1649\u001b[0m \u001b[38;5;66;03m# know when the query has finished as soon as possible.\u001b[39;00m\n\u001b[0;32m-> 1650\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reload_query_results\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mreload_query_results_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1652\u001b[0m \u001b[38;5;66;03m# Even if the query is finished now according to\u001b[39;00m\n\u001b[1;32m   1653\u001b[0m \u001b[38;5;66;03m# jobs.getQueryResults, we'll want to reload the job status if\u001b[39;00m\n\u001b[1;32m   1654\u001b[0m \u001b[38;5;66;03m# it's not already DONE.\u001b[39;00m\n\u001b[1;32m   1655\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/job/query.py:1448\u001b[0m, in \u001b[0;36mQueryJob._reload_query_results\u001b[0;34m(self, retry, timeout, page_size)\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(transport_timeout, (\u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mint\u001b[39m)):\n\u001b[1;32m   1446\u001b[0m         transport_timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1448\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_query_results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_query_results\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1449\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjob_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1450\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1451\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproject\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproject\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1452\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout_ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1453\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlocation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1454\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransport_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1455\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpage_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpage_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1456\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/client.py:2034\u001b[0m, in \u001b[0;36mClient._get_query_results\u001b[0;34m(self, job_id, retry, project, timeout_ms, location, timeout, page_size)\u001b[0m\n\u001b[1;32m   2030\u001b[0m \u001b[38;5;66;03m# This call is typically made in a polling loop that checks whether the\u001b[39;00m\n\u001b[1;32m   2031\u001b[0m \u001b[38;5;66;03m# job is complete (from QueryJob.done(), called ultimately from\u001b[39;00m\n\u001b[1;32m   2032\u001b[0m \u001b[38;5;66;03m# QueryJob.result()). So we don't need to poll here.\u001b[39;00m\n\u001b[1;32m   2033\u001b[0m span_attributes \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m\"\u001b[39m: path}\n\u001b[0;32m-> 2034\u001b[0m resource \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_api\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2035\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2036\u001b[0m \u001b[43m    \u001b[49m\u001b[43mspan_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mBigQuery.getQueryResults\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2037\u001b[0m \u001b[43m    \u001b[49m\u001b[43mspan_attributes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mspan_attributes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2038\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2039\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2040\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquery_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2041\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2042\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2043\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _QueryResults\u001b[38;5;241m.\u001b[39mfrom_api_repr(resource)\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/client.py:843\u001b[0m, in \u001b[0;36mClient._call_api\u001b[0;34m(self, retry, span_name, span_attributes, job_ref, headers, **kwargs)\u001b[0m\n\u001b[1;32m    839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m span_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    840\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m create_span(\n\u001b[1;32m    841\u001b[0m         name\u001b[38;5;241m=\u001b[39mspan_name, attributes\u001b[38;5;241m=\u001b[39mspan_attributes, client\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, job_ref\u001b[38;5;241m=\u001b[39mjob_ref\n\u001b[1;32m    842\u001b[0m     ):\n\u001b[0;32m--> 843\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    845\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m call()\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:293\u001b[0m, in \u001b[0;36mRetry.__call__.<locals>.retry_wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    289\u001b[0m target \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    290\u001b[0m sleep_generator \u001b[38;5;241m=\u001b[39m exponential_sleep_generator(\n\u001b[1;32m    291\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initial, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maximum, multiplier\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multiplier\n\u001b[1;32m    292\u001b[0m )\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mretry_target\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predicate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43msleep_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m    \u001b[49m\u001b[43mon_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:144\u001b[0m, in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sleep \u001b[38;5;129;01min\u001b[39;00m sleep_generator:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 144\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mtarget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    145\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39misawaitable(result):\n\u001b[1;32m    146\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(_ASYNC_RETRY_WARNING)\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/_http/__init__.py:482\u001b[0m, in \u001b[0;36mJSONConnection.api_request\u001b[0;34m(self, method, path, query_params, data, content_type, headers, api_base_url, api_version, expect_json, _target_object, timeout, extra_api_info)\u001b[0m\n\u001b[1;32m    479\u001b[0m     data \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mdumps(data)\n\u001b[1;32m    480\u001b[0m     content_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 482\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    483\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    484\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    485\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    486\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontent_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_target_object\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m    \u001b[49m\u001b[43mextra_api_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_api_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m300\u001b[39m:\n\u001b[1;32m    494\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mfrom_http_response(response)\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/_http/__init__.py:341\u001b[0m, in \u001b[0;36mJSONConnection._make_request\u001b[0;34m(self, method, url, data, content_type, headers, target_object, timeout, extra_api_info)\u001b[0m\n\u001b[1;32m    338\u001b[0m     headers[CLIENT_INFO_HEADER] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_agent\n\u001b[1;32m    339\u001b[0m headers[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUser-Agent\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_agent\n\u001b[0;32m--> 341\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    342\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_object\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    343\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/_http/__init__.py:379\u001b[0m, in \u001b[0;36mJSONConnection._do_request\u001b[0;34m(self, method, url, headers, data, target_object, timeout)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_do_request\u001b[39m(\n\u001b[1;32m    346\u001b[0m     \u001b[38;5;28mself\u001b[39m, method, url, headers, data, target_object, timeout\u001b[38;5;241m=\u001b[39m_DEFAULT_TIMEOUT\n\u001b[1;32m    347\u001b[0m ):  \u001b[38;5;66;03m# pylint: disable=unused-argument\u001b[39;00m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Low-level helper:  perform the actual API request over HTTP.\u001b[39;00m\n\u001b[1;32m    349\u001b[0m \n\u001b[1;32m    350\u001b[0m \u001b[38;5;124;03m    Allows batch context managers to override and defer a request.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;124;03m    :returns: The HTTP response.\u001b[39;00m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 379\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/auth/transport/requests.py:537\u001b[0m, in \u001b[0;36mAuthorizedSession.request\u001b[0;34m(self, method, url, data, headers, max_allowed_time, timeout, **kwargs)\u001b[0m\n\u001b[1;32m    534\u001b[0m remaining_time \u001b[38;5;241m=\u001b[39m guard\u001b[38;5;241m.\u001b[39mremaining_timeout\n\u001b[1;32m    536\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m TimeoutGuard(remaining_time) \u001b[38;5;28;01mas\u001b[39;00m guard:\n\u001b[0;32m--> 537\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mAuthorizedSession\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    538\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    539\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    540\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    541\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    542\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    543\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    544\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    545\u001b[0m remaining_time \u001b[38;5;241m=\u001b[39m guard\u001b[38;5;241m.\u001b[39mremaining_timeout\n\u001b[1;32m    547\u001b[0m \u001b[38;5;66;03m# If the response indicated that the credentials needed to be\u001b[39;00m\n\u001b[1;32m    548\u001b[0m \u001b[38;5;66;03m# refreshed, then refresh the credentials and re-attempt the\u001b[39;00m\n\u001b[1;32m    549\u001b[0m \u001b[38;5;66;03m# request.\u001b[39;00m\n\u001b[1;32m    550\u001b[0m \u001b[38;5;66;03m# A stored token may expire between the time it is retrieved and\u001b[39;00m\n\u001b[1;32m    551\u001b[0m \u001b[38;5;66;03m# the time the request is made, so we may need to try twice.\u001b[39;00m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    664\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    682\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/urllib3/connectionpool.py:787\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    784\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    786\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 787\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    788\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    789\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    790\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    791\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    792\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[1;32m    803\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/urllib3/connectionpool.py:534\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    532\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[1;32m    533\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 534\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    535\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    536\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n",
+            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/urllib3/connection.py:516\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    513\u001b[0m _shutdown \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshutdown\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    515\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 516\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    518\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    519\u001b[0m     assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n",
+            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/http/client.py:1428\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1426\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1427\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1428\u001b[0m         \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1429\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m   1430\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
+            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/http/client.py:331\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    329\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m    330\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 331\u001b[0m     version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    332\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m    333\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/http/client.py:292\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 292\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_MAXLINE\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    293\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m    294\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/socket.py:720\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    718\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    719\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 720\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    721\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    722\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/ssl.py:1251\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1247\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1248\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1249\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1250\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1251\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1252\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1253\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
+            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/ssl.py:1103\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1101\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1102\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1103\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1104\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1105\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
           ]
         }
       ],
       "source": [
         "for key, group in usa_data.groupby(\"date\"):\n",
         "    print(key)\n",
-        "    print(group)\n",
-        "    break"
+        "    print(group.sum(numeric_only=True))"
       ]
     },
     {

From d68b56dbe0d43763bdfc5e9b1da5594f58d80bf5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tim=20Swe=C3=B1a?= <swast@google.com>
Date: Thu, 18 Sep 2025 17:57:45 +0000
Subject: [PATCH 4/6] implement it

---
 bigframes/core/blocks.py                      |   6 +
 bigframes/core/groupby/dataframe_group_by.py  |  62 +++++-
 bigframes/core/groupby/series_group_by.py     |  57 +++++-
 bigframes/dataframe.py                        |  11 ++
 tests/unit/core/test_groupby.py               | 180 ++++++++++++++++++
 .../pandas/core/groupby/__init__.py           |  21 +-
 6 files changed, 328 insertions(+), 9 deletions(-)
 create mode 100644 tests/unit/core/test_groupby.py

diff --git a/bigframes/core/blocks.py b/bigframes/core/blocks.py
index 6e22baabec..ac313dd9bc 100644
--- a/bigframes/core/blocks.py
+++ b/bigframes/core/blocks.py
@@ -1371,10 +1371,16 @@ def aggregate(
     ) -> typing.Tuple[Block, typing.Sequence[str]]:
         """
         Apply aggregations to the block.
+
         Arguments:
             by_column_id: column id of the aggregation key, this is preserved through the transform and used as index.
             aggregations: input_column_id, operation tuples
             dropna: whether null keys should be dropped
+
+        Returns:
+            Tuple[Block, Sequence[str]]:
+                The first element is the grouped block. The second is the
+                column IDs corresponding to each applied aggregation.
         """
         if column_labels is None:
             column_labels = pd.Index(range(len(aggregations)))
diff --git a/bigframes/core/groupby/dataframe_group_by.py b/bigframes/core/groupby/dataframe_group_by.py
index 21f49fe563..f4f473093b 100644
--- a/bigframes/core/groupby/dataframe_group_by.py
+++ b/bigframes/core/groupby/dataframe_group_by.py
@@ -15,8 +15,9 @@
 from __future__ import annotations
 
 import datetime
+import functools
 import typing
-from typing import Literal, Optional, Sequence, Tuple, Union
+from typing import Iterable, Literal, Optional, Sequence, Tuple, Union
 
 import bigframes_vendored.constants as constants
 import bigframes_vendored.pandas.core.groupby as vendored_pandas_groupby
@@ -38,6 +39,8 @@
 import bigframes.core.window_spec as window_specs
 import bigframes.dataframe as df
 import bigframes.dtypes as dtypes
+import bigframes.enums
+import bigframes.operations as ops
 import bigframes.operations.aggregations as agg_ops
 import bigframes.series as series
 
@@ -54,6 +57,7 @@ def __init__(
         selected_cols: typing.Optional[typing.Sequence[str]] = None,
         dropna: bool = True,
         as_index: bool = True,
+        by_key_is_singular: bool = False,
     ):
         # TODO(tbergeron): Support more group-by expression types
         self._block = block
@@ -64,6 +68,9 @@ def __init__(
             )
         }
         self._by_col_ids = by_col_ids
+        self._by_key_is_singular = by_key_is_singular
+        if by_key_is_singular:
+            assert len(by_col_ids) == 1, "singular key should be exactly one group key"
 
         self._dropna = dropna
         self._as_index = as_index
@@ -149,6 +156,59 @@ def head(self, n: int = 5) -> df.DataFrame:
             )
         )
 
+    def __iter__(self) -> Iterable[Tuple[blocks.Label, df.DataFrame]]:
+        original_index_columns = self._block._index_columns
+        original_index_labels = self._block._index_labels
+        by_col_ids = self._by_col_ids
+        block = self._block.reset_index(
+            level=None,
+            # Keep the original index columns so they can be recovered.
+            drop=False,
+            allow_duplicates=True,
+            replacement=bigframes.enums.DefaultIndexKind.NULL,
+        ).set_index(
+            by_col_ids,
+            # Keep by_col_ids in-place so the ordering doesn't change.
+            drop=False,
+            append=False,
+        )
+        block.cached(
+            force=True,
+            # All DataFrames will be filtered by by_col_ids, so
+            # force block.cached() to cluster by the new index by explicitly
+            # setting `session_aware=False`. This will ensure that the filters
+            # are more efficient.
+            session_aware=False,
+        )
+        keys_block, _ = block.aggregate(by_col_ids, dropna=self._dropna)
+        for chunk in keys_block.to_pandas_batches():
+            for by_keys in pd.MultiIndex.from_frame(chunk.index.to_frame()):
+                filtered_df = df.DataFrame(
+                    # To ensure the cache is used, filter first, then reset the
+                    # index before yielding the DataFrame.
+                    block.filter(
+                        functools.reduce(
+                            ops.and_op.as_expr,
+                            (
+                                ops.eq_op.as_expr(by_col, ex.const(by_key))
+                                for by_col, by_key in zip(by_col_ids, by_keys)
+                            ),
+                        ),
+                    ).set_index(
+                        original_index_columns,
+                        # We retained by_col_ids in the set_index call above,
+                        # so it's safe to drop the duplicates now.
+                        drop=True,
+                        append=False,
+                        index_labels=original_index_labels,
+                    )
+                )
+
+                if self._by_key_is_singular:
+                    yield by_keys[0], filtered_df
+                else:
+                    yield by_keys, filtered_df
+
     def size(self) -> typing.Union[df.DataFrame, series.Series]:
         agg_block, _ = self._block.aggregate_size(
             by_column_ids=self._by_col_ids,
diff --git a/bigframes/core/groupby/series_group_by.py b/bigframes/core/groupby/series_group_by.py
index 8ab39d27cc..6995815112 100644
--- a/bigframes/core/groupby/series_group_by.py
+++ b/bigframes/core/groupby/series_group_by.py
@@ -15,8 +15,9 @@
 from __future__ import annotations
 
 import datetime
+import functools
 import typing
-from typing import Literal, Sequence, Union
+from typing import Iterable, Literal, Sequence, Tuple, Union
 
 import bigframes_vendored.constants as constants
 import bigframes_vendored.pandas.core.groupby as vendored_pandas_groupby
@@ -37,6 +38,8 @@
 import bigframes.core.window_spec as window_specs
 import bigframes.dataframe as df
 import bigframes.dtypes
+import bigframes.enums
+import bigframes.operations as ops
 import bigframes.operations.aggregations as agg_ops
 import bigframes.series as series
 
@@ -75,6 +78,58 @@ def head(self, n: int = 5) -> series.Series:
             )
         )
 
+    def __iter__(self) -> Iterable[Tuple[blocks.Label, series.Series]]:
+        original_index_columns = self._block._index_columns
+        original_index_labels = self._block._index_labels
+        by_col_ids = self._by_col_ids
+        block = self._block.reset_index(
+            level=None,
+            # Keep the original index columns so they can be recovered.
+            drop=False,
+            allow_duplicates=True,
+            replacement=bigframes.enums.DefaultIndexKind.NULL,
+        ).set_index(
+            by_col_ids,
+            # Keep by_col_ids in-place so the ordering doesn't change.
+            drop=False,
+            append=False,
+        )
+        block.cached(
+            force=True,
+            # All DataFrames will be filtered by by_col_ids, so
+            # force block.cached() to cluster by the new index by explicitly
+            # setting `session_aware=False`. This will ensure that the filters
+            # are more efficient.
+            session_aware=False,
+        )
+        keys_block, _ = block.aggregate(by_col_ids, dropna=self._dropna)
+        for chunk in keys_block.to_pandas_batches():
+            for by_keys in chunk.index:
+                filtered_series = series.Series(
+                    # To ensure the cache is used, filter first, then reset the
+                    # index before yielding the DataFrame.
+                    block.filter(
+                        functools.reduce(
+                            ops.and_op.as_expr,
+                            (
+                                ops.eq_op.as_expr(by_col, ex.const(by_key))
+                                for by_col, by_key in zip(by_col_ids, by_keys)
+                            ),
+                        ),
+                    )
+                    .set_index(
+                        original_index_columns,
+                        # We retained by_col_ids in the set_index call above,
+                        # so it's safe to drop the duplicates now.
+                        drop=True,
+                        append=False,
+                        index_labels=original_index_labels,
+                    )
+                    .select_column(self._value_column),
+                )
+                filtered_series.name = self._value_name
+                yield by_keys, filtered_series
+
     def all(self) -> series.Series:
         return self._aggregate(agg_ops.all_op)
 
diff --git a/bigframes/dataframe.py b/bigframes/dataframe.py
index 371f69e713..83a1025d46 100644
--- a/bigframes/dataframe.py
+++ b/bigframes/dataframe.py
@@ -3909,11 +3909,17 @@ def _groupby_level(
         as_index: bool = True,
         dropna: bool = True,
     ):
+        if utils.is_list_like(level):
+            by_key_is_singular = False
+        else:
+            by_key_is_singular = True
+
         return groupby.DataFrameGroupBy(
             self._block,
             by_col_ids=self._resolve_levels(level),
             as_index=as_index,
             dropna=dropna,
+            by_key_is_singular=by_key_is_singular,
         )
 
     def _groupby_series(
@@ -3926,10 +3932,14 @@ def _groupby_series(
         as_index: bool = True,
         dropna: bool = True,
     ):
+        # Pandas makes a distinction between groupby with a list of keys
+        # versus groupby with a single item in some methods, like __iter__.
         if not isinstance(by, bigframes.series.Series) and utils.is_list_like(by):
             by = list(by)
+            by_key_is_singular = False
         else:
             by = [typing.cast(typing.Union[blocks.Label, bigframes.series.Series], by)]
+            by_key_is_singular = True
 
         block = self._block
         col_ids: typing.Sequence[str] = []
@@ -3959,6 +3969,7 @@ def _groupby_series(
             by_col_ids=col_ids,
             as_index=as_index,
             dropna=dropna,
+            by_key_is_singular=by_key_is_singular,
         )
 
     def abs(self) -> DataFrame:
diff --git a/tests/unit/core/test_groupby.py b/tests/unit/core/test_groupby.py
new file mode 100644
index 0000000000..8a6f004642
--- /dev/null
+++ b/tests/unit/core/test_groupby.py
@@ -0,0 +1,180 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pandas as pd
+import pandas.testing
+import pytest
+
+import bigframes.core.utils as utils
+import bigframes.pandas as bpd
+
+pytest.importorskip("polars")
+pytest.importorskip("pandas", minversion="2.0.0")
+
+
+# All tests in this file require polars to be installed to pass.
+@pytest.fixture(scope="module")
+def polars_session():
+    from bigframes.testing import polars_session
+
+    return polars_session.TestSession()
+
+
+def test_groupby_df_iter_by_key_singular(polars_session):
+    pd_df = pd.DataFrame({"colA": ["a", "a", "b", "c", "c"], "colB": [1, 2, 3, 4, 5]})
+    bf_df = bpd.DataFrame(pd_df, session=polars_session)
+
+    for bf_group, pd_group in zip(bf_df.groupby("colA"), pd_df.groupby("colA")):  # type: ignore
+        bf_key, bf_group_df = bf_group
+        bf_result = bf_group_df.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_frame_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
+def test_groupby_df_iter_by_key_list(polars_session):
+    pd_df = pd.DataFrame({"colA": ["a", "a", "b", "c", "c"], "colB": [1, 2, 3, 4, 5]})
+    bf_df = bpd.DataFrame(pd_df, session=polars_session)
+
+    for bf_group, pd_group in zip(bf_df.groupby(["colA"]), pd_df.groupby(["colA"])):  # type: ignore
+        bf_key, bf_group_df = bf_group
+        bf_result = bf_group_df.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_frame_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
+def test_groupby_df_iter_by_key_list_multiple(polars_session):
+    pd_df = pd.DataFrame(
+        {
+            "colA": ["a", "a", "b", "c", "c"],
+            "colB": [1, 2, 3, 4, 5],
+            "colC": [True, False, True, False, True],
+        }
+    )
+    bf_df = bpd.DataFrame(pd_df, session=polars_session)
+
+    for bf_group, pd_group in zip(  # type: ignore
+        bf_df.groupby(["colA", "colB"]), pd_df.groupby(["colA", "colB"])
+    ):
+        bf_key, bf_group_df = bf_group
+        bf_result = bf_group_df.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_frame_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
+def test_groupby_df_iter_by_level_singular(polars_session):
+    pd_df = pd.DataFrame(
+        {"colA": ["a", "a", "b", "c", "c"], "colB": [1, 2, 3, 4, 5]}
+    ).set_index("colA")
+    bf_df = bpd.DataFrame(pd_df, session=polars_session)
+
+    for bf_group, pd_group in zip(bf_df.groupby(level=0), pd_df.groupby(level=0)):  # type: ignore
+        bf_key, bf_group_df = bf_group
+        bf_result = bf_group_df.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_frame_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
+def test_groupby_df_iter_by_level_list_one_item(polars_session):
+    pd_df = pd.DataFrame(
+        {"colA": ["a", "a", "b", "c", "c"], "colB": [1, 2, 3, 4, 5]}
+    ).set_index("colA")
+    bf_df = bpd.DataFrame(pd_df, session=polars_session)
+
+    for bf_group, pd_group in zip(bf_df.groupby(level=[0]), pd_df.groupby(level=[0])):  # type: ignore
+        bf_key, bf_group_df = bf_group
+        bf_result = bf_group_df.to_pandas()
+        pd_key, pd_result = pd_group
+
+        # In pandas 2.x, we get a warning from pandas: "Creating a Groupby
+        # object with a length-1 list-like level parameter will yield indexes
+        # as tuples in a future version. To keep indexes as scalars, create
+        # Groupby objects with a scalar level parameter instead.
+        if utils.is_list_like(pd_key):
+            assert bf_key == tuple(pd_key)
+        else:
+            assert bf_key == (pd_key,)
+        pandas.testing.assert_frame_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
+def test_groupby_df_iter_by_level_list_multiple(polars_session):
+    pd_df = pd.DataFrame(
+        {
+            "colA": ["a", "a", "b", "c", "c"],
+            "colB": [1, 2, 3, 4, 5],
+            "colC": [True, False, True, False, True],
+        }
+    ).set_index(["colA", "colB"])
+    bf_df = bpd.DataFrame(pd_df, session=polars_session)
+
+    for bf_group, pd_group in zip(  # type: ignore
+        bf_df.groupby(level=[0, 1]), pd_df.groupby(level=[0, 1])
+    ):
+        bf_key, bf_group_df = bf_group
+        bf_result = bf_group_df.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_frame_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
+def test_groupby_series_iter_by_level(polars_session):
+    series_index = ["a", "a", "b"]
+    pd_series = pd.Series([1, 2, 3], index=series_index)
+    bf_series = bpd.Series(pd_series, session=polars_session)
+    bf_series.name = pd_series.name
+
+    for bf_group, pd_group in zip(  # type: ignore
+        bf_series.groupby(level=0), pd_series.groupby(level=0)
+    ):
+        bf_key, bf_group_series = bf_group
+        bf_result = bf_group_series.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_series_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
+def test_groupby_series_iter_by_series(polars_session):
+    pd_groups = pd.Series(["a", "a", "b"])
+    bf_groups = bpd.Series(pd_groups, session=polars_session)
+    pd_series = pd.Series([1, 2, 3])
+    bf_series = bpd.Series(pd_series, session=polars_session)
+    bf_series.name = pd_series.name
+
+    for bf_group, pd_group in zip(  # type: ignore
+        bf_series.groupby(bf_groups), pd_series.groupby(pd_groups)
+    ):
+        bf_key, bf_group_series = bf_group
+        bf_result = bf_group_series.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_series_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
diff --git a/third_party/bigframes_vendored/pandas/core/groupby/__init__.py b/third_party/bigframes_vendored/pandas/core/groupby/__init__.py
index 4df2eac735..4a97a9d42d 100644
--- a/third_party/bigframes_vendored/pandas/core/groupby/__init__.py
+++ b/third_party/bigframes_vendored/pandas/core/groupby/__init__.py
@@ -1242,7 +1242,7 @@ def size(self):
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
     def __iter__(self):
-        """
+        r"""
         Groupby iterator.
 
         This method provides an iterator over the groups created by the ``resample``
@@ -1266,7 +1266,7 @@ def __iter__(self):
             b    3
             dtype: Int64
             >>> for x, y in ser.groupby(level=0):
-            ...     print(f"{x}\\n{y}\\n")
+            ...     print(f"{x}\n{y}\n")
             a
             a    1
             a    2
@@ -1284,21 +1284,28 @@ def __iter__(self):
             0  1  2  3
             1  1  5  6
             2  7  8  9
+            <BLANKLINE>
+            [3 rows x 3 columns]
             >>> for x, y in df.groupby(by=["a"]):
-            ...     print(f"{x}\\n{y}\\n")
+            ...     print(f'{x}\n{y}\n')
             (1,)
                a  b  c
             0  1  2  3
             1  1  5  6
+            <BLANKLINE>
+            [2 rows x 3 columns]
             (7,)
+            <BLANKLINE>
                a  b  c
             2  7  8  9
-
+            <BLANKLINE>
+            [1 rows x 3 columns]
+            <BLANKLINE>
 
         Returns:
-            Iterator of tuples:
-                Generator yielding a sequence of (``name``, downloaded
-                ``pandas.DataFrame`` or ``pandas.Series``) for each group.
+            Iterable[Label | Tuple, bigframes.pandas.Series | bigframes.pandas.DataFrame]:
+                Generator yielding sequence of (name, subsetted object)
+                for each group.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 

From d6ba77fa877b9bdfe6c71a37c3e00d16afbcf16d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tim=20Swe=C3=B1a?= <swast@google.com>
Date: Thu, 18 Sep 2025 18:42:54 +0000
Subject: [PATCH 5/6] refactor

---
 bigframes/core/groupby/dataframe_group_by.py | 64 ++------------
 bigframes/core/groupby/group_by.py           | 91 +++++++++++++++++++
 bigframes/core/groupby/series_group_by.py    | 72 ++++-----------
 bigframes/series.py                          |  9 ++
 tests/unit/core/test_groupby.py              | 93 +++++++++++++++++++-
 5 files changed, 219 insertions(+), 110 deletions(-)
 create mode 100644 bigframes/core/groupby/group_by.py

diff --git a/bigframes/core/groupby/dataframe_group_by.py b/bigframes/core/groupby/dataframe_group_by.py
index f4f473093b..9a1599737b 100644
--- a/bigframes/core/groupby/dataframe_group_by.py
+++ b/bigframes/core/groupby/dataframe_group_by.py
@@ -15,7 +15,6 @@
 from __future__ import annotations
 
 import datetime
-import functools
 import typing
 from typing import Iterable, Literal, Optional, Sequence, Tuple, Union
 
@@ -30,7 +29,7 @@
 from bigframes.core import log_adapter
 import bigframes.core.block_transforms as block_ops
 import bigframes.core.blocks as blocks
-from bigframes.core.groupby import aggs, series_group_by
+from bigframes.core.groupby import aggs, group_by, series_group_by
 import bigframes.core.ordering as order
 import bigframes.core.utils as utils
 import bigframes.core.validations as validations
@@ -39,8 +38,6 @@
 import bigframes.core.window_spec as window_specs
 import bigframes.dataframe as df
 import bigframes.dtypes as dtypes
-import bigframes.enums
-import bigframes.operations as ops
 import bigframes.operations.aggregations as agg_ops
 import bigframes.series as series
 
@@ -157,57 +154,14 @@ def head(self, n: int = 5) -> df.DataFrame:
         )
 
     def __iter__(self) -> Iterable[Tuple[blocks.Label, df.DataFrame]]:
-        original_index_columns = self._block._index_columns
-        original_index_labels = self._block._index_labels
-        by_col_ids = self._by_col_ids
-        block = self._block.reset_index(
-            level=None,
-            # Keep the original index columns so they can be recovered.
-            drop=False,
-            allow_duplicates=True,
-            replacement=bigframes.enums.DefaultIndexKind.NULL,
-        ).set_index(
-            by_col_ids,
-            # Keep by_col_ids in-place so the ordering doesn't change.
-            drop=False,
-            append=False,
-        )
-        block.cached(
-            force=True,
-            # All DataFrames will be filtered by by_col_ids, so
-            # force block.cached() to cluster by the new index by explicitly
-            # setting `session_aware=False`. This will ensure that the filters
-            # are more efficient.
-            session_aware=False,
-        )
-        keys_block, _ = block.aggregate(by_col_ids, dropna=self._dropna)
-        for chunk in keys_block.to_pandas_batches():
-            for by_keys in pd.MultiIndex.from_frame(chunk.index.to_frame()):
-                filtered_df = df.DataFrame(
-                    # To ensure the cache is used, filter first, then reset the
-                    # index before yielding the DataFrame.
-                    block.filter(
-                        functools.reduce(
-                            ops.and_op.as_expr,
-                            (
-                                ops.eq_op.as_expr(by_col, ex.const(by_key))
-                                for by_col, by_key in zip(by_col_ids, by_keys)
-                            ),
-                        ),
-                    ).set_index(
-                        original_index_columns,
-                        # We retained by_col_ids in the set_index call above,
-                        # so it's safe to drop the duplicates now.
-                        drop=True,
-                        append=False,
-                        index_labels=original_index_labels,
-                    )
-                )
-
-                if self._by_key_is_singular:
-                    yield by_keys[0], filtered_df
-                else:
-                    yield by_keys, filtered_df
+        for group_keys, filtered_block in group_by.block_groupby_iter(
+            self._block,
+            by_col_ids=self._by_col_ids,
+            by_key_is_singular=self._by_key_is_singular,
+            dropna=self._dropna,
+        ):
+            filtered_df = df.DataFrame(filtered_block)
+            yield group_keys, filtered_df
 
     def size(self) -> typing.Union[df.DataFrame, series.Series]:
         agg_block, _ = self._block.aggregate_size(
diff --git a/bigframes/core/groupby/group_by.py b/bigframes/core/groupby/group_by.py
new file mode 100644
index 0000000000..f00ff7c0b0
--- /dev/null
+++ b/bigframes/core/groupby/group_by.py
@@ -0,0 +1,91 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import functools
+from typing import Sequence
+
+import pandas as pd
+
+from bigframes.core import blocks
+from bigframes.core import expression as ex
+import bigframes.enums
+import bigframes.operations as ops
+
+
+def block_groupby_iter(
+    block: blocks.Block,
+    *,
+    by_col_ids: Sequence[str],
+    by_key_is_singular: bool,
+    dropna: bool,
+):
+    original_index_columns = block._index_columns
+    original_index_labels = block._index_labels
+    by_col_ids = by_col_ids
+    block = block.reset_index(
+        level=None,
+        # Keep the original index columns so they can be recovered.
+        drop=False,
+        allow_duplicates=True,
+        replacement=bigframes.enums.DefaultIndexKind.NULL,
+    ).set_index(
+        by_col_ids,
+        # Keep by_col_ids in-place so the ordering doesn't change.
+        drop=False,
+        append=False,
+    )
+    block.cached(
+        force=True,
+        # All DataFrames will be filtered by by_col_ids, so
+        # force block.cached() to cluster by the new index by explicitly
+        # setting `session_aware=False`. This will ensure that the filters
+        # are more efficient.
+        session_aware=False,
+    )
+    keys_block, _ = block.aggregate(by_col_ids, dropna=dropna)
+    for chunk in keys_block.to_pandas_batches():
+        # Convert to MultiIndex to make sure we get tuples,
+        # even for singular keys.
+        by_keys_index = chunk.index
+        if not isinstance(by_keys_index, pd.MultiIndex):
+            by_keys_index = pd.MultiIndex.from_frame(by_keys_index.to_frame())
+
+        for by_keys in by_keys_index:
+            filtered_block = (
+                # To ensure the cache is used, filter first, then reset the
+                # index before yielding the DataFrame.
+                block.filter(
+                    functools.reduce(
+                        ops.and_op.as_expr,
+                        (
+                            ops.eq_op.as_expr(by_col, ex.const(by_key))
+                            for by_col, by_key in zip(by_col_ids, by_keys)
+                        ),
+                    ),
+                ).set_index(
+                    original_index_columns,
+                    # We retained by_col_ids in the set_index call above,
+                    # so it's safe to drop the duplicates now.
+                    drop=True,
+                    append=False,
+                    index_labels=original_index_labels,
+                )
+            )
+
+            if by_key_is_singular:
+                yield by_keys[0], filtered_block
+            else:
+                yield by_keys, filtered_block
diff --git a/bigframes/core/groupby/series_group_by.py b/bigframes/core/groupby/series_group_by.py
index 6995815112..bdb34efdb5 100644
--- a/bigframes/core/groupby/series_group_by.py
+++ b/bigframes/core/groupby/series_group_by.py
@@ -15,7 +15,6 @@
 from __future__ import annotations
 
 import datetime
-import functools
 import typing
 from typing import Iterable, Literal, Sequence, Tuple, Union
 
@@ -29,7 +28,7 @@
 from bigframes.core import log_adapter
 import bigframes.core.block_transforms as block_ops
 import bigframes.core.blocks as blocks
-from bigframes.core.groupby import aggs
+from bigframes.core.groupby import aggs, group_by
 import bigframes.core.ordering as order
 import bigframes.core.utils as utils
 import bigframes.core.validations as validations
@@ -38,8 +37,6 @@
 import bigframes.core.window_spec as window_specs
 import bigframes.dataframe as df
 import bigframes.dtypes
-import bigframes.enums
-import bigframes.operations as ops
 import bigframes.operations.aggregations as agg_ops
 import bigframes.series as series
 
@@ -55,6 +52,8 @@ def __init__(
         by_col_ids: typing.Sequence[str],
         value_name: blocks.Label = None,
         dropna=True,
+        *,
+        by_key_is_singular: bool = False,
     ):
         # TODO(tbergeron): Support more group-by expression types
         self._block = block
@@ -63,6 +62,10 @@ def __init__(
         self._value_name = value_name
         self._dropna = dropna  # Applies to aggregations but not windowing
 
+        self._by_key_is_singular = by_key_is_singular
+        if by_key_is_singular:
+            assert len(by_col_ids) == 1, "singular key should be exactly one group key"
+
     @property
     def _session(self) -> session.Session:
         return self._block.session
@@ -79,56 +82,17 @@ def head(self, n: int = 5) -> series.Series:
         )
 
     def __iter__(self) -> Iterable[Tuple[blocks.Label, series.Series]]:
-        original_index_columns = self._block._index_columns
-        original_index_labels = self._block._index_labels
-        by_col_ids = self._by_col_ids
-        block = self._block.reset_index(
-            level=None,
-            # Keep the original index columns so they can be recovered.
-            drop=False,
-            allow_duplicates=True,
-            replacement=bigframes.enums.DefaultIndexKind.NULL,
-        ).set_index(
-            by_col_ids,
-            # Keep by_col_ids in-place so the ordering doesn't change.
-            drop=False,
-            append=False,
-        )
-        block.cached(
-            force=True,
-            # All DataFrames will be filtered by by_col_ids, so
-            # force block.cached() to cluster by the new index by explicitly
-            # setting `session_aware=False`. This will ensure that the filters
-            # are more efficient.
-            session_aware=False,
-        )
-        keys_block, _ = block.aggregate(by_col_ids, dropna=self._dropna)
-        for chunk in keys_block.to_pandas_batches():
-            for by_keys in chunk.index:
-                filtered_series = series.Series(
-                    # To ensure the cache is used, filter first, then reset the
-                    # index before yielding the DataFrame.
-                    block.filter(
-                        functools.reduce(
-                            ops.and_op.as_expr,
-                            (
-                                ops.eq_op.as_expr(by_col, ex.const(by_key))
-                                for by_col, by_key in zip(by_col_ids, by_keys)
-                            ),
-                        ),
-                    )
-                    .set_index(
-                        original_index_columns,
-                        # We retained by_col_ids in the set_index call above,
-                        # so it's safe to drop the duplicates now.
-                        drop=True,
-                        append=False,
-                        index_labels=original_index_labels,
-                    )
-                    .select_column(self._value_column),
-                )
-                filtered_series.name = self._value_name
-                yield by_keys, filtered_series
+        for group_keys, filtered_block in group_by.block_groupby_iter(
+            self._block,
+            by_col_ids=self._by_col_ids,
+            by_key_is_singular=self._by_key_is_singular,
+            dropna=self._dropna,
+        ):
+            filtered_series = series.Series(
+                filtered_block.select_column(self._value_column)
+            )
+            filtered_series.name = self._value_name
+            yield group_keys, filtered_series
 
     def all(self) -> series.Series:
         return self._aggregate(agg_ops.all_op)
diff --git a/bigframes/series.py b/bigframes/series.py
index da2f3f07c4..4e51181617 100644
--- a/bigframes/series.py
+++ b/bigframes/series.py
@@ -1854,12 +1854,18 @@ def _groupby_level(
         level: int | str | typing.Sequence[int] | typing.Sequence[str],
         dropna: bool = True,
     ) -> bigframes.core.groupby.SeriesGroupBy:
+        if utils.is_list_like(level):
+            by_key_is_singular = False
+        else:
+            by_key_is_singular = True
+
         return groupby.SeriesGroupBy(
             self._block,
             self._value_column,
             by_col_ids=self._resolve_levels(level),
             value_name=self.name,
             dropna=dropna,
+            by_key_is_singular=by_key_is_singular,
         )
 
     def _groupby_values(
@@ -1871,8 +1877,10 @@ def _groupby_values(
     ) -> bigframes.core.groupby.SeriesGroupBy:
         if not isinstance(by, Series) and _is_list_like(by):
             by = list(by)
+            by_key_is_singular = False
         else:
             by = [typing.cast(typing.Union[blocks.Label, Series], by)]
+            by_key_is_singular = True
 
         block = self._block
         grouping_cols: typing.Sequence[str] = []
@@ -1904,6 +1912,7 @@ def _groupby_values(
             by_col_ids=grouping_cols,
             value_name=self.name,
             dropna=dropna,
+            by_key_is_singular=by_key_is_singular,
         )
 
     def apply(
diff --git a/tests/unit/core/test_groupby.py b/tests/unit/core/test_groupby.py
index 8a6f004642..8df0e5344e 100644
--- a/tests/unit/core/test_groupby.py
+++ b/tests/unit/core/test_groupby.py
@@ -143,7 +143,7 @@ def test_groupby_df_iter_by_level_list_multiple(polars_session):
         )
 
 
-def test_groupby_series_iter_by_level(polars_session):
+def test_groupby_series_iter_by_level_singular(polars_session):
     series_index = ["a", "a", "b"]
     pd_series = pd.Series([1, 2, 3], index=series_index)
     bf_series = bpd.Series(pd_series, session=polars_session)
@@ -161,6 +161,56 @@ def test_groupby_series_iter_by_level(polars_session):
         )
 
 
+def test_groupby_series_iter_by_level_list_one_item(polars_session):
+    series_index = ["a", "a", "b"]
+    pd_series = pd.Series([1, 2, 3], index=series_index)
+    bf_series = bpd.Series(pd_series, session=polars_session)
+    bf_series.name = pd_series.name
+
+    for bf_group, pd_group in zip(  # type: ignore
+        bf_series.groupby(level=[0]), pd_series.groupby(level=[0])
+    ):
+        bf_key, bf_group_series = bf_group
+        bf_result = bf_group_series.to_pandas()
+        pd_key, pd_result = pd_group
+
+        # In pandas 2.x, we get a warning from pandas: "Creating a Groupby
+        # object with a length-1 list-like level parameter will yield indexes
+        # as tuples in a future version. To keep indexes as scalars, create
+        # Groupby objects with a scalar level parameter instead.
+        if utils.is_list_like(pd_key):
+            assert bf_key == tuple(pd_key)
+        else:
+            assert bf_key == (pd_key,)
+        pandas.testing.assert_series_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
+def test_groupby_series_iter_by_level_list_multiple(polars_session):
+    pd_df = pd.DataFrame(
+        {
+            "colA": ["a", "a", "b", "c", "c"],
+            "colB": [1, 2, 3, 4, 5],
+            "colC": [True, False, True, False, True],
+        }
+    ).set_index(["colA", "colB"])
+    pd_series = pd_df["colC"]
+    bf_df = bpd.DataFrame(pd_df, session=polars_session)
+    bf_series = bf_df["colC"]
+
+    for bf_group, pd_group in zip(  # type: ignore
+        bf_series.groupby(level=[0, 1]), pd_series.groupby(level=[0, 1])
+    ):
+        bf_key, bf_group_df = bf_group
+        bf_result = bf_group_df.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_series_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
 def test_groupby_series_iter_by_series(polars_session):
     pd_groups = pd.Series(["a", "a", "b"])
     bf_groups = bpd.Series(pd_groups, session=polars_session)
@@ -178,3 +228,44 @@ def test_groupby_series_iter_by_series(polars_session):
         pandas.testing.assert_series_equal(
             bf_result, pd_result, check_dtype=False, check_index_type=False
         )
+
+
+def test_groupby_series_iter_by_series_list_one_item(polars_session):
+    pd_groups = pd.Series(["a", "a", "b"])
+    bf_groups = bpd.Series(pd_groups, session=polars_session)
+    pd_series = pd.Series([1, 2, 3])
+    bf_series = bpd.Series(pd_series, session=polars_session)
+    bf_series.name = pd_series.name
+
+    for bf_group, pd_group in zip(  # type: ignore
+        bf_series.groupby([bf_groups]), pd_series.groupby([pd_groups])
+    ):
+        bf_key, bf_group_series = bf_group
+        bf_result = bf_group_series.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_series_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )
+
+
+def test_groupby_series_iter_by_series_list_multiple(polars_session):
+    pd_group_a = pd.Series(["a", "a", "b", "c", "c"])
+    bf_group_a = bpd.Series(pd_group_a, session=polars_session)
+    pd_group_b = pd.Series([0, 0, 0, 1, 1])
+    bf_group_b = bpd.Series(pd_group_b, session=polars_session)
+    pd_series = pd.Series([1, 2, 3, 4, 5])
+    bf_series = bpd.Series(pd_series, session=polars_session)
+    bf_series.name = pd_series.name
+
+    for bf_group, pd_group in zip(  # type: ignore
+        bf_series.groupby([bf_group_a, bf_group_b]),
+        pd_series.groupby([pd_group_a, pd_group_b]),
+    ):
+        bf_key, bf_group_series = bf_group
+        bf_result = bf_group_series.to_pandas()
+        pd_key, pd_result = pd_group
+        assert bf_key == pd_key
+        pandas.testing.assert_series_equal(
+            bf_result, pd_result, check_dtype=False, check_index_type=False
+        )

From b4214cf405fa25ae226db2cd257da09b23c5c5cc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tim=20Swe=C3=B1a?= <swast@google.com>
Date: Thu, 18 Sep 2025 18:44:12 +0000
Subject: [PATCH 6/6] revert notebook change

---
 .../bq_dataframes_covid_line_graphs.ipynb     | 573 +-----------------
 1 file changed, 10 insertions(+), 563 deletions(-)

diff --git a/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb b/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb
index ce505c477e..d69aecd8c3 100644
--- a/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb
+++ b/notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb
@@ -206,7 +206,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 4,
       "metadata": {
         "id": "R7STCS8xB5d2"
       },
@@ -216,7 +216,7 @@
         "\n",
         "# Note: The project option is not required in all environments.\n",
         "# On BigQuery Studio, the project ID is automatically detected.\n",
-        "# bpd.options.bigquery.project = PROJECT_ID\n",
+        "bpd.options.bigquery.project = PROJECT_ID\n",
         "\n",
         "# Note: The location option is not required.\n",
         "# It defaults to the location of the first table or query\n",
@@ -238,24 +238,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 5,
       "metadata": {
         "id": "zDSwoBo1CU3G"
       },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "Query job 173629b3-e364-4a4c-bad2-859aa16534ff is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:173629b3-e364-4a4c-bad2-859aa16534ff&page=queryresults\">Open Job</a>"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "all_data = bpd.read_gbq(\"bigquery-public-data.covid19_open_data.covid19_open_data\")"
       ]
@@ -271,7 +258,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 6,
       "metadata": {
         "id": "UjMT_qhjf8Fu"
       },
@@ -295,91 +282,9 @@
       "metadata": {
         "id": "IaoUf57ZwrJ8"
       },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "Query job 994fc4c0-dde9-4d41-b3d4-c500a4185567 is DONE. 372.9 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:994fc4c0-dde9-4d41-b3d4-c500a4185567&page=queryresults\">Open Job</a>"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>date</th>\n",
-              "      <th>new_confirmed</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>2020-10-08</td>\n",
-              "      <td>189</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2020-12-20</td>\n",
-              "      <td>148</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2021-01-16</td>\n",
-              "      <td>2543</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>2020-08-15</td>\n",
-              "      <td>933</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2020-05-30</td>\n",
-              "      <td>790</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "         date  new_confirmed\n",
-              "0  2020-10-08            189\n",
-              "1  2020-12-20            148\n",
-              "2  2021-01-16           2543\n",
-              "3  2020-08-15            933\n",
-              "4  2020-05-30            790"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
-        "usa_data = usa_data[[\"date\", \"new_confirmed\"]]\n",
-        "usa_data.peek()"
+        "usa_data = usa_data[[\"date\", \"new_confirmed\"]]"
       ]
     },
     {
@@ -403,465 +308,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
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-          "data": {
-            "text/html": [
-              "Query job 470f5b46-59c4-4db7-9244-5d6afb930b4b is DONE. 72.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:470f5b46-59c4-4db7-9244-5d6afb930b4b&page=queryresults\">Open Job</a>"
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-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "2020-01-01\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "Query job 78f73d75-cc26-4481-b51e-55a4f22c2a1e is DONE. 46.4 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:78f73d75-cc26-4481-b51e-55a4f22c2a1e&page=queryresults\">Open Job</a>"
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-            "text/html": [
-              "Query job 2ce6dc96-f036-46e2-a5d7-4b655f3d1b88 is DONE. 31 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:2ce6dc96-f036-46e2-a5d7-4b655f3d1b88&page=queryresults\">Open Job</a>"
-            ],
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-              "<IPython.core.display.HTML object>"
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-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "new_confirmed    0\n",
-            "dtype: Int64\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "Query job d156d38c-81cc-4e14-86b1-e37d38191c6a is DONE. 72.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:d156d38c-81cc-4e14-86b1-e37d38191c6a&page=queryresults\">Open Job</a>"
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-          "output_type": "stream",
-          "text": [
-            "2020-01-02\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "Query job 0b3b0a9f-a3f6-4cd1-8c13-ee74d3acb67d is DONE. 46.4 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:0b3b0a9f-a3f6-4cd1-8c13-ee74d3acb67d&page=queryresults\">Open Job</a>"
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-              "Query job cfc7c265-50bf-4867-8ec7-3c910f82fb4e is DONE. 31 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:cfc7c265-50bf-4867-8ec7-3c910f82fb4e&page=queryresults\">Open Job</a>"
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-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "new_confirmed    0\n",
-            "dtype: Int64\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "Query job a00fc9c5-828b-4467-9daa-f415257722bd is DONE. 72.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:a00fc9c5-828b-4467-9daa-f415257722bd&page=queryresults\">Open Job</a>"
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-          "text": [
-            "2020-01-03\n"
-          ]
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-            "new_confirmed    0\n",
-            "dtype: Int64\n"
-          ]
-        },
-        {
-          "data": {
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-              "Query job 7ffd7934-2f13-4640-a676-a1e3ff63d60a is DONE. 72.5 MB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:7ffd7934-2f13-4640-a676-a1e3ff63d60a&page=queryresults\">Open Job</a>"
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-          "text": [
-            "2020-01-04\n"
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-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "new_confirmed    0\n",
-            "dtype: Int64\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
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-            "2020-01-05\n"
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-          "data": {
-            "text/html": [
-              "Query job 1e3ef3bb-c1dd-4c37-8921-42e4743f486a is RUNNING. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=swast-scratch&j=bq:US:1e3ef3bb-c1dd-4c37-8921-42e4743f486a&page=queryresults\">Open Job</a>"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Requested cancellation for Query job 1e3ef3bb-c1dd-4c37-8921-42e4743f486a in location US...\n"
-          ]
-        },
-        {
-          "ename": "KeyboardInterrupt",
-          "evalue": "",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "Cell \u001b[0;32mIn[9], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, group \u001b[38;5;129;01min\u001b[39;00m usa_data\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(key)\n\u001b[0;32m----> 3\u001b[0m     \u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/log_adapter.py:147\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    144\u001b[0m _call_stack\u001b[38;5;241m.\u001b[39mappend(full_method_name)\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mNotImplementedError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;66;03m# Log method parameters that are implemented in pandas but either missing (TypeError)\u001b[39;00m\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;66;03m# or not fully supported (NotImplementedError) in BigFrames.\u001b[39;00m\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;66;03m# Logging is currently supported only when we can access the bqclient through\u001b[39;00m\n\u001b[1;32m    152\u001b[0m     \u001b[38;5;66;03m# self._block.expr.session.bqclient. Also, to avoid generating multiple queries\u001b[39;00m\n\u001b[1;32m    153\u001b[0m     \u001b[38;5;66;03m# because of internal calls, we log only when the method is directly invoked.\u001b[39;00m\n\u001b[1;32m    154\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_block\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(_call_stack) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/series.py:345\u001b[0m, in \u001b[0;36mSeries.__repr__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m opts\u001b[38;5;241m.\u001b[39mrepr_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeferred\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    343\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m formatter\u001b[38;5;241m.\u001b[39mrepr_query_job(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_dry_run())\n\u001b[0;32m--> 345\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cached\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    346\u001b[0m pandas_df, _, query_job \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_block\u001b[38;5;241m.\u001b[39mretrieve_repr_request_results(max_results)\n\u001b[1;32m    347\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_internal_query_job(query_job)\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/log_adapter.py:147\u001b[0m, in \u001b[0;36mmethod_logger.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    144\u001b[0m _call_stack\u001b[38;5;241m.\u001b[39mappend(full_method_name)\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mNotImplementedError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;66;03m# Log method parameters that are implemented in pandas but either missing (TypeError)\u001b[39;00m\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;66;03m# or not fully supported (NotImplementedError) in BigFrames.\u001b[39;00m\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;66;03m# Logging is currently supported only when we can access the bqclient through\u001b[39;00m\n\u001b[1;32m    152\u001b[0m     \u001b[38;5;66;03m# self._block.expr.session.bqclient. Also, to avoid generating multiple queries\u001b[39;00m\n\u001b[1;32m    153\u001b[0m     \u001b[38;5;66;03m# because of internal calls, we log only when the method is directly invoked.\u001b[39;00m\n\u001b[1;32m    154\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_block\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(_call_stack) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/series.py:2071\u001b[0m, in \u001b[0;36mSeries._cached\u001b[0;34m(self, force, session_aware)\u001b[0m\n\u001b[1;32m   2070\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_cached\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m, force: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m, session_aware: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series:\n\u001b[0;32m-> 2071\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_block\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcached\u001b[49m\u001b[43m(\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msession_aware\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msession_aware\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2072\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/blocks.py:2440\u001b[0m, in \u001b[0;36mBlock.cached\u001b[0;34m(self, force, session_aware)\u001b[0m\n\u001b[1;32m   2438\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Write the block to a session table.\"\"\"\u001b[39;00m\n\u001b[1;32m   2439\u001b[0m \u001b[38;5;66;03m# use a heuristic for whether something needs to be cached\u001b[39;00m\n\u001b[0;32m-> 2440\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_executor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2441\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2442\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2443\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_session\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msession_aware\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2444\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcluster_cols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2445\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:459\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor.cached\u001b[0;34m(self, array_value, force, use_session, cluster_cols)\u001b[0m\n\u001b[1;32m    457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    458\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_session:\n\u001b[0;32m--> 459\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache_with_session_awareness\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    460\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    461\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache_with_cluster_cols(array_value, cluster_cols\u001b[38;5;241m=\u001b[39mcluster_cols)\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:584\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor._cache_with_session_awareness\u001b[0;34m(self, array_value)\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache_with_offsets(bigframes\u001b[38;5;241m.\u001b[39mcore\u001b[38;5;241m.\u001b[39mArrayValue(target))\n\u001b[1;32m    583\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 584\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache_with_cluster_cols\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbigframes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mArrayValue\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:537\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor._cache_with_cluster_cols\u001b[0;34m(self, array_value, cluster_cols)\u001b[0m\n\u001b[1;32m    532\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Executes the query and uses the resulting table to rewrite future executions.\"\"\"\u001b[39;00m\n\u001b[1;32m    534\u001b[0m sql, schema, ordering_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompiler\u001b[38;5;241m.\u001b[39mcompile_raw(\n\u001b[1;32m    535\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreplace_cached_subtrees(array_value\u001b[38;5;241m.\u001b[39mnode)\n\u001b[1;32m    536\u001b[0m )\n\u001b[0;32m--> 537\u001b[0m tmp_table \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sql_as_cached_temp_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    538\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    539\u001b[0m \u001b[43m    \u001b[49m\u001b[43mschema\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    540\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcluster_cols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbq_io\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect_cluster_cols\u001b[49m\u001b[43m(\u001b[49m\u001b[43mschema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcluster_cols\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    541\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    542\u001b[0m cached_replacement \u001b[38;5;241m=\u001b[39m array_value\u001b[38;5;241m.\u001b[39mas_cached(\n\u001b[1;32m    543\u001b[0m     cache_table\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbqclient\u001b[38;5;241m.\u001b[39mget_table(tmp_table),\n\u001b[1;32m    544\u001b[0m     ordering\u001b[38;5;241m=\u001b[39mordering_info,\n\u001b[1;32m    545\u001b[0m )\u001b[38;5;241m.\u001b[39mnode\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_executions[array_value\u001b[38;5;241m.\u001b[39mnode] \u001b[38;5;241m=\u001b[39m cached_replacement\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:631\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor._sql_as_cached_temp_table\u001b[0;34m(self, sql, schema, cluster_cols)\u001b[0m\n\u001b[1;32m    626\u001b[0m job_config \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m    627\u001b[0m     bigquery\u001b[38;5;241m.\u001b[39mQueryJobConfig,\n\u001b[1;32m    628\u001b[0m     bigquery\u001b[38;5;241m.\u001b[39mQueryJobConfig\u001b[38;5;241m.\u001b[39mfrom_api_repr({}),\n\u001b[1;32m    629\u001b[0m )\n\u001b[1;32m    630\u001b[0m job_config\u001b[38;5;241m.\u001b[39mdestination \u001b[38;5;241m=\u001b[39m temp_table\n\u001b[0;32m--> 631\u001b[0m _, query_job \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_execute_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    632\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    633\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjob_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjob_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[43m    \u001b[49m\u001b[43mapi_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcached\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    635\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    636\u001b[0m query_job\u001b[38;5;241m.\u001b[39mdestination\n\u001b[1;32m    637\u001b[0m query_job\u001b[38;5;241m.\u001b[39mresult()\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/executor.py:497\u001b[0m, in \u001b[0;36mBigQueryCachingExecutor._run_execute_query\u001b[0;34m(self, sql, job_config, api_name, page_size, max_results)\u001b[0m\n\u001b[1;32m    495\u001b[0m bq_io\u001b[38;5;241m.\u001b[39madd_and_trim_labels(job_config, api_name\u001b[38;5;241m=\u001b[39mapi_name)\n\u001b[1;32m    496\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 497\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbq_io\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart_query_with_client\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbqclient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    499\u001b[0m \u001b[43m        \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjob_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjob_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mapi_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_results\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpage_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpage_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetrics\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    505\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    507\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m google\u001b[38;5;241m.\u001b[39mapi_core\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mBadRequest \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    508\u001b[0m     \u001b[38;5;66;03m# Unfortunately, this error type does not have a separate error code or exception type\u001b[39;00m\n\u001b[1;32m    509\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResources exceeded during query execution\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m e\u001b[38;5;241m.\u001b[39mmessage:\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/session/_io/bigquery/__init__.py:253\u001b[0m, in \u001b[0;36mstart_query_with_client\u001b[0;34m(bq_client, sql, job_config, location, project, max_results, page_size, timeout, api_name, metrics)\u001b[0m\n\u001b[1;32m    251\u001b[0m opts \u001b[38;5;241m=\u001b[39m bigframes\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mdisplay\n\u001b[1;32m    252\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m opts\u001b[38;5;241m.\u001b[39mprogress_bar \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m query_job\u001b[38;5;241m.\u001b[39mconfiguration\u001b[38;5;241m.\u001b[39mdry_run:\n\u001b[0;32m--> 253\u001b[0m     results_iterator \u001b[38;5;241m=\u001b[39m \u001b[43mformatting_helpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_for_query_job\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_job\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_results\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopts\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpage_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpage_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    260\u001b[0m     results_iterator \u001b[38;5;241m=\u001b[39m query_job\u001b[38;5;241m.\u001b[39mresult(\n\u001b[1;32m    261\u001b[0m         max_results\u001b[38;5;241m=\u001b[39mmax_results, page_size\u001b[38;5;241m=\u001b[39mpage_size\n\u001b[1;32m    262\u001b[0m     )\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/bigframes/formatting_helpers.py:139\u001b[0m, in \u001b[0;36mwait_for_query_job\u001b[0;34m(query_job, max_results, page_size, progress_bar)\u001b[0m\n\u001b[1;32m    137\u001b[0m loading_bar \u001b[38;5;241m=\u001b[39m display\u001b[38;5;241m.\u001b[39mHTML(get_query_job_loading_html(query_job))\n\u001b[1;32m    138\u001b[0m display\u001b[38;5;241m.\u001b[39mdisplay(loading_bar, display_id\u001b[38;5;241m=\u001b[39mdisplay_id)\n\u001b[0;32m--> 139\u001b[0m query_result \u001b[38;5;241m=\u001b[39m \u001b[43mquery_job\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_results\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpage_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpage_size\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    142\u001b[0m query_job\u001b[38;5;241m.\u001b[39mreload()\n\u001b[1;32m    143\u001b[0m display\u001b[38;5;241m.\u001b[39mupdate_display(\n\u001b[1;32m    144\u001b[0m     display\u001b[38;5;241m.\u001b[39mHTML(get_query_job_loading_html(query_job)),\n\u001b[1;32m    145\u001b[0m     display_id\u001b[38;5;241m=\u001b[39mdisplay_id,\n\u001b[1;32m    146\u001b[0m )\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/job/query.py:1681\u001b[0m, in \u001b[0;36mQueryJob.result\u001b[0;34m(self, page_size, max_results, retry, timeout, start_index, job_retry)\u001b[0m\n\u001b[1;32m   1676\u001b[0m     remaining_timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remaining_timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1679\u001b[0m     \u001b[38;5;66;03m# Since is_job_done() calls jobs.getQueryResults, which is a\u001b[39;00m\n\u001b[1;32m   1680\u001b[0m     \u001b[38;5;66;03m# long-running API, don't delay the next request at all.\u001b[39;00m\n\u001b[0;32m-> 1681\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mis_job_done\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1682\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m   1683\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1684\u001b[0m     \u001b[38;5;66;03m# Use a monotonic clock since we don't actually care about\u001b[39;00m\n\u001b[1;32m   1685\u001b[0m     \u001b[38;5;66;03m# daylight savings or similar, just the elapsed time.\u001b[39;00m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:293\u001b[0m, in \u001b[0;36mRetry.__call__.<locals>.retry_wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    289\u001b[0m target \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    290\u001b[0m sleep_generator \u001b[38;5;241m=\u001b[39m exponential_sleep_generator(\n\u001b[1;32m    291\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initial, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maximum, multiplier\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multiplier\n\u001b[1;32m    292\u001b[0m )\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mretry_target\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predicate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43msleep_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m    \u001b[49m\u001b[43mon_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:144\u001b[0m, in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sleep \u001b[38;5;129;01min\u001b[39;00m sleep_generator:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 144\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mtarget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    145\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39misawaitable(result):\n\u001b[1;32m    146\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(_ASYNC_RETRY_WARNING)\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/job/query.py:1650\u001b[0m, in \u001b[0;36mQueryJob.result.<locals>.is_job_done\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m   1646\u001b[0m \u001b[38;5;66;03m# Call jobs.getQueryResults with max results set to 0 just to\u001b[39;00m\n\u001b[1;32m   1647\u001b[0m \u001b[38;5;66;03m# wait for the query to finish. Unlike most methods,\u001b[39;00m\n\u001b[1;32m   1648\u001b[0m \u001b[38;5;66;03m# jobs.getQueryResults hangs as long as it can to ensure we\u001b[39;00m\n\u001b[1;32m   1649\u001b[0m \u001b[38;5;66;03m# know when the query has finished as soon as possible.\u001b[39;00m\n\u001b[0;32m-> 1650\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reload_query_results\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mreload_query_results_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1652\u001b[0m \u001b[38;5;66;03m# Even if the query is finished now according to\u001b[39;00m\n\u001b[1;32m   1653\u001b[0m \u001b[38;5;66;03m# jobs.getQueryResults, we'll want to reload the job status if\u001b[39;00m\n\u001b[1;32m   1654\u001b[0m \u001b[38;5;66;03m# it's not already DONE.\u001b[39;00m\n\u001b[1;32m   1655\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/job/query.py:1448\u001b[0m, in \u001b[0;36mQueryJob._reload_query_results\u001b[0;34m(self, retry, timeout, page_size)\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(transport_timeout, (\u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mint\u001b[39m)):\n\u001b[1;32m   1446\u001b[0m         transport_timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1448\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_query_results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_query_results\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1449\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjob_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1450\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1451\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproject\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproject\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1452\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout_ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1453\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlocation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1454\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransport_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1455\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpage_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpage_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1456\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/client.py:2034\u001b[0m, in \u001b[0;36mClient._get_query_results\u001b[0;34m(self, job_id, retry, project, timeout_ms, location, timeout, page_size)\u001b[0m\n\u001b[1;32m   2030\u001b[0m \u001b[38;5;66;03m# This call is typically made in a polling loop that checks whether the\u001b[39;00m\n\u001b[1;32m   2031\u001b[0m \u001b[38;5;66;03m# job is complete (from QueryJob.done(), called ultimately from\u001b[39;00m\n\u001b[1;32m   2032\u001b[0m \u001b[38;5;66;03m# QueryJob.result()). So we don't need to poll here.\u001b[39;00m\n\u001b[1;32m   2033\u001b[0m span_attributes \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m\"\u001b[39m: path}\n\u001b[0;32m-> 2034\u001b[0m resource \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_api\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2035\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2036\u001b[0m \u001b[43m    \u001b[49m\u001b[43mspan_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mBigQuery.getQueryResults\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2037\u001b[0m \u001b[43m    \u001b[49m\u001b[43mspan_attributes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mspan_attributes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2038\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2039\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2040\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquery_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2041\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2042\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2043\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _QueryResults\u001b[38;5;241m.\u001b[39mfrom_api_repr(resource)\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/bigquery/client.py:843\u001b[0m, in \u001b[0;36mClient._call_api\u001b[0;34m(self, retry, span_name, span_attributes, job_ref, headers, **kwargs)\u001b[0m\n\u001b[1;32m    839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m span_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    840\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m create_span(\n\u001b[1;32m    841\u001b[0m         name\u001b[38;5;241m=\u001b[39mspan_name, attributes\u001b[38;5;241m=\u001b[39mspan_attributes, client\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, job_ref\u001b[38;5;241m=\u001b[39mjob_ref\n\u001b[1;32m    842\u001b[0m     ):\n\u001b[0;32m--> 843\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    845\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m call()\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:293\u001b[0m, in \u001b[0;36mRetry.__call__.<locals>.retry_wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    289\u001b[0m target \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    290\u001b[0m sleep_generator \u001b[38;5;241m=\u001b[39m exponential_sleep_generator(\n\u001b[1;32m    291\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initial, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maximum, multiplier\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multiplier\n\u001b[1;32m    292\u001b[0m )\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mretry_target\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predicate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43msleep_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m    \u001b[49m\u001b[43mon_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/api_core/retry/retry_unary.py:144\u001b[0m, in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sleep \u001b[38;5;129;01min\u001b[39;00m sleep_generator:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 144\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mtarget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    145\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39misawaitable(result):\n\u001b[1;32m    146\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(_ASYNC_RETRY_WARNING)\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/_http/__init__.py:482\u001b[0m, in \u001b[0;36mJSONConnection.api_request\u001b[0;34m(self, method, path, query_params, data, content_type, headers, api_base_url, api_version, expect_json, _target_object, timeout, extra_api_info)\u001b[0m\n\u001b[1;32m    479\u001b[0m     data \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mdumps(data)\n\u001b[1;32m    480\u001b[0m     content_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 482\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    483\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    484\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    485\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    486\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontent_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_target_object\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m    \u001b[49m\u001b[43mextra_api_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_api_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m300\u001b[39m:\n\u001b[1;32m    494\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mfrom_http_response(response)\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/_http/__init__.py:341\u001b[0m, in \u001b[0;36mJSONConnection._make_request\u001b[0;34m(self, method, url, data, content_type, headers, target_object, timeout, extra_api_info)\u001b[0m\n\u001b[1;32m    338\u001b[0m     headers[CLIENT_INFO_HEADER] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_agent\n\u001b[1;32m    339\u001b[0m headers[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUser-Agent\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_agent\n\u001b[0;32m--> 341\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    342\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_object\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    343\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/cloud/_http/__init__.py:379\u001b[0m, in \u001b[0;36mJSONConnection._do_request\u001b[0;34m(self, method, url, headers, data, target_object, timeout)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_do_request\u001b[39m(\n\u001b[1;32m    346\u001b[0m     \u001b[38;5;28mself\u001b[39m, method, url, headers, data, target_object, timeout\u001b[38;5;241m=\u001b[39m_DEFAULT_TIMEOUT\n\u001b[1;32m    347\u001b[0m ):  \u001b[38;5;66;03m# pylint: disable=unused-argument\u001b[39;00m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Low-level helper:  perform the actual API request over HTTP.\u001b[39;00m\n\u001b[1;32m    349\u001b[0m \n\u001b[1;32m    350\u001b[0m \u001b[38;5;124;03m    Allows batch context managers to override and defer a request.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;124;03m    :returns: The HTTP response.\u001b[39;00m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 379\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/google/auth/transport/requests.py:537\u001b[0m, in \u001b[0;36mAuthorizedSession.request\u001b[0;34m(self, method, url, data, headers, max_allowed_time, timeout, **kwargs)\u001b[0m\n\u001b[1;32m    534\u001b[0m remaining_time \u001b[38;5;241m=\u001b[39m guard\u001b[38;5;241m.\u001b[39mremaining_timeout\n\u001b[1;32m    536\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m TimeoutGuard(remaining_time) \u001b[38;5;28;01mas\u001b[39;00m guard:\n\u001b[0;32m--> 537\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mAuthorizedSession\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    538\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    539\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    540\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    541\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    542\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    543\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    544\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    545\u001b[0m remaining_time \u001b[38;5;241m=\u001b[39m guard\u001b[38;5;241m.\u001b[39mremaining_timeout\n\u001b[1;32m    547\u001b[0m \u001b[38;5;66;03m# If the response indicated that the credentials needed to be\u001b[39;00m\n\u001b[1;32m    548\u001b[0m \u001b[38;5;66;03m# refreshed, then refresh the credentials and re-attempt the\u001b[39;00m\n\u001b[1;32m    549\u001b[0m \u001b[38;5;66;03m# request.\u001b[39;00m\n\u001b[1;32m    550\u001b[0m \u001b[38;5;66;03m# A stored token may expire between the time it is retrieved and\u001b[39;00m\n\u001b[1;32m    551\u001b[0m \u001b[38;5;66;03m# the time the request is made, so we may need to try twice.\u001b[39;00m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    664\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    682\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/urllib3/connectionpool.py:787\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    784\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    786\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 787\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    788\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    789\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    790\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    791\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    792\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[1;32m    803\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/urllib3/connectionpool.py:534\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    532\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[1;32m    533\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 534\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    535\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    536\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n",
-            "File \u001b[0;32m~/src/github.com/googleapis/python-bigquery-dataframes/venv/lib/python3.12/site-packages/urllib3/connection.py:516\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    513\u001b[0m _shutdown \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshutdown\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    515\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 516\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    518\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    519\u001b[0m     assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n",
-            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/http/client.py:1428\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1426\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1427\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1428\u001b[0m         \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1429\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m   1430\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
-            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/http/client.py:331\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    329\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m    330\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 331\u001b[0m     version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    332\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m    333\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/http/client.py:292\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 292\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_MAXLINE\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    293\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m    294\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/socket.py:720\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    718\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    719\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 720\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    721\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    722\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/ssl.py:1251\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1247\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1248\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1249\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1250\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1251\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1252\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1253\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
-            "File \u001b[0;32m~/.pyenv/versions/3.12.6/lib/python3.12/ssl.py:1103\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1101\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1102\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1103\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1104\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1105\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-          ]
-        }
-      ],
-      "source": [
-        "for key, group in usa_data.groupby(\"date\"):\n",
-        "    print(key)\n",
-        "    print(group.sum(numeric_only=True))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 8,
       "metadata": {
         "id": "tYDoaKgJChiq"
       },
@@ -891,7 +338,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 9,
       "metadata": {
         "id": "gFbCgfFC2gHw"
       },
@@ -902,7 +349,7 @@
               "<Axes: xlabel='date', ylabel='New Cases'>"
             ]
           },
-          "execution_count": 8,
+          "execution_count": 9,
           "metadata": {},
           "output_type": "execute_result"
         },