8000 `tensorflow`: add `tf.linalg` module by hoel-bagard · Pull Request #11386 · python/typeshed · GitHub
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tensorflow: add tf.linalg module #11386

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Merged
merged 5 commits into from
Feb 17, 2024
Merged

tensorflow: add tf.linalg module #11386

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02a6721
Add some linalg stubs
hoel-bagard Feb 9, 2024
a5372f0
Update stubs/tensorflow/tensorflow/linalg.pyi
hoel-bagard Feb 17, 2024
28ae404
fix: re-export eye from tf
hoel-bagard Feb 17, 2024
6e2d07a
fix: eye's batch_shape
hoel-bagard Feb 17, 2024
ece7680
add and use Integer type alias.
hoel-bagard Feb 17, 2024
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50 changes: 50 additions & 0 deletions stubs/tensorflow/tensorflow/linalg.pyi
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Original file line number Diff line number Diff line change
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from _typeshed import Incomplete
from builtins import bool as _bool
from typing import Literal, overload

from tensorflow import RaggedTensor, Tensor, norm as norm
from tensorflow._aliases import DTypeLike, ScalarTensorCompatible, TensorCompatible
from tensorflow.math import l2_normalize as l2_normalize

@overload
def matmul(
a: TensorCompatible,
b: TensorCompatible,
transpose_a: _bool = False,
transpose_b: _bool = False,
adjoint_a: _bool = False,
adjoint_b: _bool = False,
a_is_sparse: _bool = False,
b_is_sparse: _bool = False,
output_type: DTypeLike | None = None,
name: str | None = None,
) -> Tensor: ...
@overload
def matmul(
a: RaggedTensor,
b: RaggedTensor,
transpose_a: _bool = False,
transpose_b: _bool = False,
adjoint_a: _bool = False,
adjoint_b: _bool = False,
a_is_sparse: _bool = False,
b_is_sparse: _bool = False,
output_type: DTypeLike | None = None,
name: str | None = None,
) -> RaggedTensor: ...
def set_diag(
input: TensorCompatible,
diagonal: TensorCompatible,
name: str | None = "set_diag",
k: int = 0,
align: Literal["RIGHT_LEFT", "RIGHT_RIGHT", "LEFT_LEFT", "LEFT_RIGHT"] = "RIGHT_LEFT",
) -> Tensor: ...
def eye(
num_rows: ScalarTensorCompatible,
num_columns: ScalarTensorCompatible | None = None,
batch_shape: int | tuple[int, ...] | None = None,
dtype: DTypeLike = ...,
name: str | None = None,
) -> Tensor: ...
def band_part(input: TensorCompatible, num_lower: int, num_upper: int, name: str | None = None) -> Tensor: ...
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A scalar (0d like tf.constant(10)) int tensor will also work for num_lower/num_upper. This is generally true of most tensor functions that take an int, although I'm unsure how common it is and higher rank tensors will fail. I'd weakly lean to prefer ScalarTensorCompatible over int here and in similar cases.

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I also noticed that in #11333 and there I added an Integer type for that purpose. It seems common enough in TensorFlow to warrant an alias.

Integer: TypeAlias = tf.Tensor | int | IntArray | np.number[Any]

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I've done the same here in ece7680.

def __getattr__(name: str) -> Incomplete: ...
0