8000 added some missing attribute annotations + formatting · pytorch/pytorch@73f160e · GitHub
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randolf-scholzrandolf-scholz
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added some missing attribute annotations + formatting
1 parent 480a5a0 commit 73f160e

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25 files changed

+31
-0
lines changed
  • gumbel.py
  • half_normal.py
  • independent.py
  • inverse_gamma.py
  • laplace.py
  • lkj_cholesky.py
  • log_normal.py
  • logistic_normal.py
  • lowrank_multivariate_normal.py
  • multinomial.py
  • multivariate_normal.py
  • pareto.py
  • relaxed_bernoulli.py
  • relaxed_categorical.py
  • studentT.py
  • transformed_distribution.py
  • transforms.py
  • uniform.py
  • weibull.py

    • 25 files changed

    +31
    -0
    lines changed

    ‎torch/distributions/binomial.py

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    @@ -54,6 +54,8 @@ class Binomial(Distribution):
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    }
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    has_enumerate_support: bool = True
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    total_count: Tensor
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    def __init__(
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    self,
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    total_count: Union[Tensor, int] = 1,

    ‎torch/distributions/dirichlet.py

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    @@ -58,6 +58,7 @@ class Dirichlet(ExponentialFamily):
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    }
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    support: ClassVar[constraints.Simplex] = constraints.simplex
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    has_rsample: bool = True
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    concentration: Tensor
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    def __init__(

    ‎torch/distributions/exponential.py

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    @@ -34,6 +34,7 @@ class Exponential(ExponentialFamily):
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    support: ClassVar[constraints.NonNegative] = constraints.nonnegative
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    has_rsample: bool = True
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    _mean_carrier_measure: float = 0
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    rate: Tensor
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    @property

    ‎torch/distributions/fishersnedecor.py

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    @@ -36,6 +36,7 @@ class FisherSnedecor(Distribution):
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    }
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    support: ClassVar[constraints.Positive] = constraints.positive
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    has_rsample: bool = True
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    df1: Tensor
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    df2: Tensor
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    ‎torch/distributions/gamma.py

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    @@ -42,6 +42,7 @@ class Gamma(ExponentialFamily):
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    support: ClassVar[constraints.NonNegative] = constraints.nonnegative
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    has_rsample: bool = True
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    _mean_carrier_measure: float = 0
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    concentration: Tensor
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    rate: Tensor
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    ‎torch/distributions/generalized_pareto.py

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    @@ -44,6 +44,10 @@ class GeneralizedPareto(Distribution):
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    }
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    has_rsample: bool = True
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    loc: Tensor
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    scale: Tensor
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    concentration: Tensor
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    def __init__(
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    self,
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    loc: Union[float, Tensor],

    ‎torch/distributions/gumbel.py

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    @@ -38,6 +38,9 @@ class Gumbel(TransformedDistribution):
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    }
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    support: ClassVar[constraints.Real] = constraints.real # type: ignore[assignment]
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    loc: Tensor
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    scale: Tensor
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    def __init__(
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    self,
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    loc: Union[Tensor, float],

    ‎torch/distributions/half_normal.py

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    @@ -38,6 +38,7 @@ class HalfNormal(TransformedDistribution):
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    }
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    support: ClassVar[constraints.NonNegative] = constraints.nonnegative # type: ignore[assignment]
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    has_rsample: bool = True
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    base_dist: Normal
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    def __init__(

    ‎torch/distributions/independent.py

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    @@ -47,6 +47,7 @@ class Independent(Distribution, Generic[D]):
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    """
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    arg_constraints: ClassVar[dict[str, Constraint]] = {}
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    base_dist: D
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    def __init__(

    ‎torch/distributions/inverse_gamma.py

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    @@ -42,6 +42,7 @@ class InverseGamma(TransformedDistribution):
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    }
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    support: ClassVar[constraints.Positive] = constraints.positive # type: ignore[assignment]
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    has_rsample: bool = True
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    base_dist: Gamma
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    def __init__(

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