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__all__ module-attribute 

__all__ = ['enable', 'disable', 'no_weight_grad', 'conv2d', 'conv_transpose2d']

__author__ module-attribute 

__author__ = 'Peter Yuen'

__email__ module-attribute 

__email__ = 'ppeetteerrsx@gmail.com'

__version__ module-attribute 

__version__ = metadata.version('torch_conv_gradfix')

no_weight_grad module-attribute 

no_weight_grad: Type[ContextManager[None]] = None

__docs 

__docs()

Build gh-pages documentation branch.

Source code in torch_conv_gradfix/__init__.py 
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def __docs():  # pragma: no cover
    """
    Build gh-pages documentation branch.
    """
    shell(
        "cp README.md docs/index.md && \
            mkdocs gh-deploy --force"
    )

__serve 

__serve()

Serve local documentation.

Source code in torch_conv_gradfix/__init__.py 
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def __serve():  # pragma: no cover
    """
    Serve local documentation.
    """
    shell(
        "cp README.md docs/index.md && \
            mkdocs serve"
    )

__test 

__test()

Runs pytest locally and keeps only coverage.xml for GitHub Actions to upload to Codecov.

Source code in torch_conv_gradfix/__init__.py 
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def __test():  # pragma: no cover
    """
    Runs pytest locally and keeps only `coverage.xml` for GitHub Actions to upload to Codecov.
    """
    shell(
        "pytest --cov=torch_conv_gradfix --cov-report xml --cov-report term-missing tests \
            && rm -rf .pytest_cache && rm .coverage"
    )

conv2d 

conv2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: IntPairOr = 1, padding: IntPairOr = 0, dilation: IntPairOr = 1, groups: int = 1) -> Tensor
Source code in torch_conv_gradfix/namespace.py 
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def conv2d(
    input: Tensor,
    weight: Tensor,
    bias: Optional[Tensor] = None,
    stride: IntPairOr = 1,
    padding: IntPairOr = 0,
    dilation: IntPairOr = 1,
    groups: int = 1,
) -> Tensor:  # pragma: no cover
    ...

conv_transpose2d 

conv_transpose2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: IntPairOr = 1, padding: IntPairOr = 0, output_padding: IntPairOr = 0, dilation: IntPairOr = 1, groups: int = 1) -> Tensor
Source code in torch_conv_gradfix/namespace.py 
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def conv_transpose2d(
    input: Tensor,
    weight: Tensor,
    bias: Optional[Tensor] = None,
    stride: IntPairOr = 1,
    padding: IntPairOr = 0,
    output_padding: IntPairOr = 0,
    dilation: IntPairOr = 1,
    groups: int = 1,
) -> Tensor:  # pragma: no cover
    ...

disable 

disable()
Source code in torch_conv_gradfix/namespace.py 
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def disable():  # pragma: no cover
    ...

enable 

enable()
Source code in torch_conv_gradfix/namespace.py 
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def enable():  # pragma: no cover
    ...