Docstring Agent Skill

r"""function_name(param1, param2, *, kwarg1=default1, kwarg2=default2) -> ReturnType

r"""conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 2D convolution over an input image composed of several input
planes.

.. math::
    \text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}

See :class:`~torch.nn.Conv2d` for details and output shape.

.. note::
    This function doesn't work directly with NLLLoss,
    which expects the Log to be computed between the Softmax and itself.
    Use log_softmax instead (it's faster and has better numerical properties).

.. warning::
    :func:`new_tensor` always copies :attr:`data`. If you have a Tensor
    ``data`` and want to avoid a copy, use :func:`torch.Tensor.requires_grad_`
    or :func:`torch.Tensor.detach`.

Args:
    input (Tensor): input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
    weight (Tensor): filters of shape :math:`(\text{out\_channels} , kH , kW)`
    bias (Tensor, optional): optional bias tensor of shape :math:`(\text{out\_channels})`. Default: ``None``
    stride (int or tuple): the stride of the convolving kernel. Can be a single number or a
      tuple `(sH, sW)`. Default: 1

Keyword args:
    dtype (:class:`torch.dtype`, optional): the desired type of returned tensor.
        Default: if None, same :class:`torch.dtype` as this tensor.
    device (:class:`torch.device`, optional): the desired device of returned tensor.
        Default: if None, same :class:`torch.device` as this tensor.
    requires_grad (bool, optional): If autograd should record operations on the
        returned tensor. Default: ``False``.

Returns:
    Tensor: Sampled tensor of same shape as `logits` from the Gumbel-Softmax distribution.
        If ``hard=True``, the returned samples will be one-hot, otherwise they will
        be probability distributions that sum to 1 across `dim`.

Examples::

    >>> inputs = torch.randn(33, 16, 30)
    >>> filters = torch.randn(20, 16, 5)
    >>> F.conv1d(inputs, filters)

    >>> # With square kernels and equal stride
    >>> filters = torch.randn(8, 4, 3, 3)
    >>> inputs = torch.randn(1, 4, 5, 5)
    >>> F.conv2d(inputs, filters, padding=1)

.. _Link Name:
    https://arxiv.org/abs/1611.00712

def relu(input: Tensor, inplace: bool = False) -> Tensor:
    r"""relu(input, inplace=False) -> Tensor

    Applies the rectified linear unit function element-wise. See
    :class:`~torch.nn.ReLU` for more details.
    """
    # implementation

conv1d = _add_docstr(
    torch.conv1d,
    r"""
conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 1D convolution over an input signal composed of several input
planes.

See :class:`~torch.nn.Conv1d` for details and output shape.

Args:
    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
    weight: filters of shape :math:`(\text{out\_channels} , kW)`
    ...
""",
)

add_docstr_all(
    "abs_",
    r"""
abs_() -> Tensor

In-place version of :meth:`~Tensor.abs`
""",
)

add_docstr_all(
    "absolute",
    r"""
absolute() -> Tensor

Alias for :func:`abs`
""",
)

:math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`

common_args = parse_kwargs(
    """
    dtype (:class:`torch.dtype`, optional): the desired type of returned tensor.
        Default: if None, same as this tensor.
"""
)

# Then use with .format():
r"""
...

Keyword args:
    {dtype}
    {device}
""".format(**common_args)

r"""
{tf32_note}

{cudnn_reproducibility_note}
""".format(**reproducibility_notes, **tf32_notes)

def gumbel_softmax(
    logits: Tensor,
    tau: float = 1,
    hard: bool = False,
    eps: float = 1e-10,
    dim: int = -1,
) -> Tensor:
    r"""
    Sample from the Gumbel-Softmax distribution and optionally discretize.

    Args:
        logits (Tensor): `[..., num_features]` unnormalized log probabilities
        tau (float): non-negative scalar temperature
        hard (bool): if ``True``, the returned samples will be discretized as one-hot vectors,
              but will be differentiated as if it is the soft sample in autograd. Default: ``False``
        dim (int): A dimension along which softmax will be computed. Default: -1

    Returns:
        Tensor: Sampled tensor of same shape as `logits` from the Gumbel-Softmax distribution.
            If ``hard=True``, the returned samples will be one-hot, otherwise they will
            be probability distributions that sum to 1 across `dim`.

    .. note::
        This function is here for legacy reasons, may be removed from nn.Functional in the future.

    Examples::
        >>> logits = torch.randn(20, 32)
        >>> # Sample soft categorical using reparametrization trick:
        >>> F.gumbel_softmax(logits, tau=1, hard=False)
        >>> # Sample hard categorical using "Straight-through" trick:
        >>> F.gumbel_softmax(logits, tau=1, hard=True)

    .. _Link 1:
        https://arxiv.org/abs/1611.00712
    """
    # implementation