_tensor_docs.py

"""Adds docstrings to Tensor functions"""

import torch._C
from torch._C import _add_docstr as add_docstr
from ._torch_docs import parse_kwargs


def add_docstr_all(method, docstr):
    add_docstr(getattr(torch._C._TensorBase, method), docstr)

common_args = parse_kwargs("""
    memory_format (:class:`torch.memory_format`, optional): the desired memory format of
        returned Tensor. Default: ``torch.preserve_format``.
""")

new_common_args = parse_kwargs("""
    size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
        shape of the output tensor.
    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``.
    pin_memory (bool, optional): If set, returned tensor would be allocated in
        the pinned memory. Works only for CPU tensors. Default: ``False``.
""")

add_docstr_all('new_tensor',
               r"""
new_tensor(data, dtype=None, device=None, requires_grad=False) -> Tensor

Returns a new Tensor with :attr:`data` as the tensor data.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.

.. 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`.
    If you have a numpy array and want to avoid a copy, use
    :func:`torch.from_numpy`.

.. warning::

    When data is a tensor `x`, :func:`new_tensor()` reads out 'the data' from whatever it is passed,
    and constructs a leaf variable. Therefore ``tensor.new_tensor(x)`` is equivalent to ``x.clone().detach()``
    and ``tensor.new_tensor(x, requires_grad=True)`` is equivalent to ``x.clone().detach().requires_grad_(True)``.
    The equivalents using ``clone()`` and ``detach()`` are recommended.

Args:
    data (array_like): The returned Tensor copies :attr:`data`.
    {dtype}
    {device}
    {requires_grad}

Example::

    >>> tensor = torch.ones((2,), dtype=torch.int8)
    >>> data = [[0, 1], [2, 3]]
    >>> tensor.new_tensor(data)
    tensor([[ 0,  1],
            [ 2,  3]], dtype=torch.int8)

""".format(**new_common_args))

add_docstr_all('new_full',
               r"""
new_full(size, fill_value, dtype=None, device=None, requires_grad=False) -> Tensor

Returns a Tensor of size :attr:`size` filled with :attr:`fill_value`.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.

Args:
    fill_value (scalar): the number to fill the output tensor with.
    {dtype}
    {device}
    {requires_grad}

Example::

    >>> tensor = torch.ones((2,), dtype=torch.float64)
    >>> tensor.new_full((3, 4), 3.141592)
    tensor([[ 3.1416,  3.1416,  3.1416,  3.1416],
            [ 3.1416,  3.1416,  3.1416,  3.1416],
            [ 3.1416,  3.1416,  3.1416,  3.1416]], dtype=torch.float64)

""".format(**new_common_args))

add_docstr_all('new_empty',
               r"""
new_empty(size, dtype=None, device=None, requires_grad=False) -> Tensor

Returns a Tensor of size :attr:`size` filled with uninitialized data.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.

Args:
    {dtype}
    {device}
    {requires_grad}

Example::

    >>> tensor = torch.ones(())
    >>> tensor.new_empty((2, 3))
    tensor([[ 5.8182e-18,  4.5765e-41, -1.0545e+30],
            [ 3.0949e-41,  4.4842e-44,  0.0000e+00]])

""".format(**new_common_args))

add_docstr_all('new_ones',
               r"""
new_ones(size, dtype=None, device=None, requires_grad=False) -> Tensor

Returns a Tensor of size :attr:`size` filled with ``1``.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.

Args:
    size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
        shape of the output tensor.
    {dtype}
    {device}
    {requires_grad}

Example::

    >>> tensor = torch.tensor((), dtype=torch.int32)
    >>> tensor.new_ones((2, 3))
    tensor([[ 1,  1,  1],
            [ 1,  1,  1]], dtype=torch.int32)

""".format(**new_common_args))

add_docstr_all('new_zeros',
               r"""
new_zeros(size, dtype=None, device=None, requires_grad=False) -> Tensor

Returns a Tensor of size :attr:`size` filled with ``0``.
By default, the returned Tensor has the same :class:`torch.dtype` and
:class:`torch.device` as this tensor.

Args:
    size (int...): a list, tuple, or :class:`torch.Size` of integers defining the
        shape of the output tensor.
    {dtype}
    {device}
    {requires_grad}

Example::

    >>> tensor = torch.tensor((), dtype=torch.float64)
    >>> tensor.new_zeros((2, 3))
    tensor([[ 0.,  0.,  0.],
            [ 0.,  0.,  0.]], dtype=torch.float64)

""".format(**new_common_args))

add_docstr_all('abs',
               r"""
abs() -> Tensor

See :func:`torch.abs`
""")

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`
""")

add_docstr_all('absolute_',
               r"""
absolute_() -> Tensor

In-place version of :meth:`~Tensor.absolute`
Alias for :func:`abs_`
""")

add_docstr_all('acos',
               r"""
acos() -> Tensor

See :func:`torch.acos`
""")

add_docstr_all('acos_',
               r"""
acos_() -> Tensor

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

add_docstr_all('acosh',
               r"""
acosh() -> Tensor

See :func:`torch.acosh`
""")

add_docstr_all('acosh_',
               r"""
acosh_() -> Tensor

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

add_docstr_all('add',
               r"""
add(other, *, alpha=1) -> Tensor

Add a scalar or tensor to :attr:`self` tensor. If both :attr:`alpha`
and :attr:`other` are specified, each element of :attr:`other` is scaled by
:attr:`alpha` before being used.

When :attr:`other` is a tensor, the shape of :attr:`other` must be
:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
tensor

See :func:`torch.add`
""")

add_docstr_all('add_',
               r"""
add_(other, *, alpha=1) -> Tensor

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

add_docstr_all('addbmm',
               r"""
addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor

See :func:`torch.addbmm`
""")

add_docstr_all('addbmm_',
               r"""
addbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor

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

add_docstr_all('addcdiv',
               r"""
addcdiv(tensor1, tensor2, *, value=1) -> Tensor

See :func:`torch.addcdiv`
""")

add_docstr_all('addcdiv_',
               r"""
addcdiv_(tensor1, tensor2, *, value=1) -> Tensor

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

add_docstr_all('addcmul',
               r"""
addcmul(tensor1, tensor2, *, value=1) -> Tensor

See :func:`torch.addcmul`
""")

add_docstr_all('addcmul_',
               r"""
addcmul_(tensor1, tensor2, *, value=1) -> Tensor

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

add_docstr_all('addmm',
               r"""
addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor

See :func:`torch.addmm`
""")

add_docstr_all('addmm_',
               r"""
addmm_(mat1, mat2, *, beta=1, alpha=1) -> Tensor

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

add_docstr_all('addmv',
               r"""
addmv(mat, vec, *, beta=1, alpha=1) -> Tensor

See :func:`torch.addmv`
""")

add_docstr_all('addmv_',
               r"""
addmv_(mat, vec, *, beta=1, alpha=1) -> Tensor

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

add_docstr_all('addr',
               r"""
addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor

See :func:`torch.addr`
""")

add_docstr_all('addr_',
               r"""
addr_(vec1, vec2, *, beta=1, alpha=1) -> Tensor

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

add_docstr_all('align_as',
               r"""
align_as(other) -> Tensor

Permutes the dimensions of the :attr:`self` tensor to match the dimension order
in the :attr:`other` tensor, adding size-one dims for any new names.

This operation is useful for explicit broadcasting by names (see examples).

All of the dims of :attr:`self` must be named in order to use this method.
The resulting tensor is a view on the original tensor.

All dimension names of :attr:`self` must be present in ``other.names``.
:attr:`other` may contain named dimensions that are not in ``self.names``;
the output tensor has a size-one dimension for each of those new names.

To align a tensor to a specific order, use :meth:`~Tensor.align_to`.

Examples::

    # Example 1: Applying a mask
    >>> mask = torch.randint(2, [127, 128], dtype=torch.bool).refine_names('W', 'H')
    >>> imgs = torch.randn(32, 128, 127, 3, names=('N', 'H', 'W', 'C'))
    >>> imgs.masked_fill_(mask.align_as(imgs), 0)


    # Example 2: Applying a per-channel-scale
    >>> def scale_channels(input, scale):
    >>>    scale = scale.refine_names('C')
    >>>    return input * scale.align_as(input)

    >>> num_channels = 3
    >>> scale = torch.randn(num_channels, names=('C',))
    >>> imgs = torch.rand(32, 128, 128, num_channels, names=('N', 'H', 'W', 'C'))
    >>> more_imgs = torch.rand(32, num_channels, 128, 128, names=('N', 'C', 'H', 'W'))
    >>> videos = torch.randn(3, num_channels, 128, 128, 128, names=('N', 'C', 'H', 'W', 'D'))

    # scale_channels is agnostic to the dimension order of the input
    >>> scale_channels(imgs, scale)
    >>> scale_channels(more_imgs, scale)
    >>> scale_channels(videos, scale)

.. warning::
    The named tensor API is experimental and subject to change.

""")

add_docstr_all('all',
               r"""
.. function:: all() -> bool

Returns True if all elements in the tensor are True, False otherwise.

Example::

    >>> a = torch.rand(1, 2).bool()
    >>> a
    tensor([[False, True]], dtype=torch.bool)
    >>> a.all()
    tensor(False, dtype=torch.bool)

.. function:: all(dim, keepdim=False, out=None) -> Tensor

Returns True if all elements in each row of the tensor in the given
dimension :attr:`dim` are True, False otherwise.

If :attr:`keepdim` is ``True``, the output tensor is of the same size as
:attr:`input` except in the dimension :attr:`dim` where it is of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
in the output tensor having 1 fewer dimension than :attr:`input`.

Args:
    dim (int): the dimension to reduce
    keepdim (bool): whether the output tensor has :attr:`dim` retained or not
    out (Tensor, optional): the output tensor

Example::

    >>> a = torch.rand(4, 2).bool()
    >>> a
    tensor([[True, True],
            [True, False],
            [True, True],
            [True, True]], dtype=torch.bool)
    >>> a.all(dim=1)
    tensor([ True, False,  True,  True], dtype=torch.bool)
    >>> a.all(dim=0)
    tensor([ True, False], dtype=torch.bool)
""")

add_docstr_all('allclose',
               r"""
allclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor

See :func:`torch.allclose`
""")

add_docstr_all('angle',
               r"""
angle() -> Tensor

See :func:`torch.angle`
""")

add_docstr_all('any',
               r"""
.. function:: any() -> bool

Returns True if any elements in the tensor are True, False otherwise.

Example::

    >>> a = torch.rand(1, 2).bool()
    >>> a
    tensor([[False, True]], dtype=torch.bool)
    >>> a.any()
    tensor(True, dtype=torch.bool)

.. function:: any(dim, keepdim=False, out=None) -> Tensor

Returns True if any elements in each row of the tensor in the given
dimension :attr:`dim` are True, False otherwise.

If :attr:`keepdim` is ``True``, the output tensor is of the same size as
:attr:`input` except in the dimension :attr:`dim` where it is of size 1.
Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting
in the output tensor having 1 fewer dimension than :attr:`input`.

Args:
    dim (int): the dimension to reduce
    keepdim (bool): whether the output tensor has :attr:`dim` retained or not
    out (Tensor, optional): the output tensor

Example::

    >>> a = torch.randn(4, 2) < 0
    >>> a
    tensor([[ True,  True],
            [False,  True],
            [ True,  True],
            [False, False]])
    >>> a.any(1)
    tensor([ True,  True,  True, False])
    >>> a.any(0)
    tensor([True, True])
""")

add_docstr_all('apply_',
               r"""
apply_(callable) -> Tensor

Applies the function :attr:`callable` to each element in the tensor, replacing
each element with the value returned by :attr:`callable`.

.. note::

    This function only works with CPU tensors and should not be used in code
    sections that require high performance.
""")

add_docstr_all('asin', r"""
asin() -> Tensor

See :func:`torch.asin`
""")

add_docstr_all('asin_',
               r"""
asin_() -> Tensor

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

add_docstr_all('asinh', r"""
asinh() -> Tensor

See :func:`torch.asinh`
""")

add_docstr_all('asinh_',
               r"""
asinh_() -> Tensor

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

add_docstr_all('as_strided', r"""
as_strided(size, stride, storage_offset=0) -> Tensor

See :func:`torch.as_strided`
""")

add_docstr_all('atan',
               r"""
atan() -> Tensor

See :func:`torch.atan`
""")

add_docstr_all('atan2',
               r"""
atan2(other) -> Tensor

See :func:`torch.atan2`
""")

add_docstr_all('atan2_',
               r"""
atan2_(other) -> Tensor

In-place version of :meth:`~Tensor.atan2`
""")

add_docstr_all('atan_',
               r"""
atan_() -> Tensor

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

add_docstr_all('atanh',
               r"""
atanh() -> Tensor

See :func:`torch.atanh`
""")

add_docstr_all('atanh_',
               r"""
In-place version of :meth:`~Tensor.atanh`
""")

add_docstr_all('baddbmm',
               r"""
baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor

See :func:`torch.baddbmm`
""")

add_docstr_all('baddbmm_',
               r"""
baddbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor

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

add_docstr_all('bernoulli',
               r"""
bernoulli(*, generator=None) -> Tensor

Returns a result tensor where each :math:`\texttt{result[i]}` is independently
sampled from :math:`\text{Bernoulli}(\texttt{self[i]})`. :attr:`self` must have
floating point ``dtype``, and the result will have the same ``dtype``.

See :func:`torch.bernoulli`
""")

add_docstr_all('bernoulli_',
               r"""
.. function:: bernoulli_(p=0.5, *, generator=None) -> Tensor

    Fills each location of :attr:`self` with an independent sample from
    :math:`\text{Bernoulli}(\texttt{p})`. :attr:`self` can have integral
    ``dtype``.

.. function:: bernoulli_(p_tensor, *, generator=None) -> Tensor

    :attr:`p_tensor` should be a tensor containing probabilities to be used for
    drawing the binary random number.

    The :math:`\text{i}^{th}` element of :attr:`self` tensor will be set to a
    value sampled from :math:`\text{Bernoulli}(\texttt{p\_tensor[i]})`.

    :attr:`self` can have integral ``dtype``, but :attr:`p_tensor` must have
    floating point ``dtype``.

See also :meth:`~Tensor.bernoulli` and :func:`torch.bernoulli`
""")

add_docstr_all('bincount',
               r"""
bincount(weights=None, minlength=0) -> Tensor

See :func:`torch.bincount`
""")

add_docstr_all('bitwise_not',
               r"""
bitwise_not() -> Tensor

See :func:`torch.bitwise_not`
""")

add_docstr_all('bitwise_not_',
               r"""
bitwise_not_() -> Tensor

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

add_docstr_all('bitwise_and',
               r"""
bitwise_and() -> Tensor

See :func:`torch.bitwise_and`
""")

add_docstr_all('bitwise_and_',
               r"""
bitwise_and_() -> Tensor

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

add_docstr_all('bitwise_or',
               r"""
bitwise_or() -> Tensor

See :func:`torch.bitwise_or`
""")

add_docstr_all('bitwise_or_',
               r"""
bitwise_or_() -> Tensor

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

add_docstr_all('bitwise_xor',
               r"""
bitwise_xor() -> Tensor

See :func:`torch.bitwise_xor`
""")

add_docstr_all('bitwise_xor_',
               r"""
bitwise_xor_() -> Tensor

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

add_docstr_all('logical_and',
               r"""
logical_and() -> Tensor

See :func:`torch.logical_and`
""")

add_docstr_all('logical_and_',
               r"""
logical_and_() -> Tensor

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

add_docstr_all('logical_not',
               r"""
logical_not() -> Tensor

See :func:`torch.logical_not`
""")

add_docstr_all('logical_not_',
               r"""
logical_not_() -> Tensor

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

add_docstr_all('logical_or',
               r"""
logical_or() -> Tensor

See :func:`torch.logical_or`
""")

add_docstr_all('logical_or_',
               r"""
logical_or_() -> Tensor

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

add_docstr_all('logical_xor',
               r"""
logical_xor() -> Tensor

See :func:`torch.logical_xor`
""")

add_docstr_all('logical_xor_',
               r"""
logical_xor_() -> Tensor

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

add_docstr_all('bmm',
               r"""
bmm(batch2) -> Tensor

See :func:`torch.bmm`
""")

add_docstr_all('cauchy_',
               r"""
cauchy_(median=0, sigma=1, *, generator=None) -> Tensor

Fills the tensor with numbers drawn from the Cauchy distribution:

.. math::

    f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
""")

add_docstr_all('ceil',
               r"""
ceil() -> Tensor

See :func:`torch.ceil`
""")

add_docstr_all('ceil_',
               r"""
ceil_() -> Tensor

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

add_docstr_all('cholesky',
               r"""
cholesky(upper=False) -> Tensor

See :func:`torch.cholesky`
""")

add_docstr_all('cholesky_solve',
               r"""
cholesky_solve(input2, upper=False) -> Tensor

See :func:`torch.cholesky_solve`
""")

add_docstr_all('cholesky_inverse',
               r"""
cholesky_inverse(upper=False) -> Tensor

See :func:`torch.cholesky_inverse`
""")

add_docstr_all('clamp',
               r"""
clamp(min, max) -> Tensor

See :func:`torch.clamp`
""")

add_docstr_all('clamp_',
               r"""
clamp_(min, max) -> Tensor

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

add_docstr_all('clone',
               r"""
clone(memory_format=torch.preserve_format) -> Tensor

Returns a copy of the :attr:`self` tensor. The copy has the same size and data
type as :attr:`self`.

.. note::

    Unlike `copy_()`, this function is recorded in the computation graph. Gradients
    propagating to the cloned tensor will propagate to the original tensor.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('contiguous',
               r"""
contiguous(memory_format=torch.contiguous_format) -> Tensor

Returns a contiguous in memory tensor containing the same data as :attr:`self` tensor. If
:attr:`self` tensor is already in the specified memory format, this function returns the
:attr:`self` tensor.

Args:
    memory_format (:class:`torch.memory_format`, optional): the desired memory format of
        returned Tensor. Default: ``torch.contiguous_format``.
""")

add_docstr_all('copy_',
               r"""
copy_(src, non_blocking=False) -> Tensor

Copies the elements from :attr:`src` into :attr:`self` tensor and returns
:attr:`self`.

The :attr:`src` tensor must be :ref:`broadcastable <broadcasting-semantics>`
with the :attr:`self` tensor. It may be of a different data type or reside on a
different device.

Args:
    src (Tensor): the source tensor to copy from
    non_blocking (bool): if ``True`` and this copy is between CPU and GPU,
        the copy may occur asynchronously with respect to the host. For other
        cases, this argument has no effect.
""")

add_docstr_all('conj',
               r"""
conj() -> Tensor

See :func:`torch.conj`
""")

add_docstr_all('cos',
               r"""
cos() -> Tensor

See :func:`torch.cos`
""")

add_docstr_all('cos_',
               r"""
cos_() -> Tensor

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

add_docstr_all('cosh',
               r"""
cosh() -> Tensor

See :func:`torch.cosh`
""")

add_docstr_all('cosh_',
               r"""
cosh_() -> Tensor

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

add_docstr_all('cpu',
               r"""
cpu(memory_format=torch.preserve_format) -> Tensor

Returns a copy of this object in CPU memory.

If this object is already in CPU memory and on the correct device,
then no copy is performed and the original object is returned.

Args:
    {memory_format}

""".format(**common_args))

add_docstr_all('count_nonzero',
               r"""
count_nonzero(dim=None) -> Tensor

See :func:`torch.count_nonzero`
""")

add_docstr_all('cross',
               r"""
cross(other, dim=-1) -> Tensor

See :func:`torch.cross`
""")

add_docstr_all('cuda',
               r"""
cuda(device=None, non_blocking=False, memory_format=torch.preserve_format) -> Tensor

Returns a copy of this object in CUDA memory.

If this object is already in CUDA memory and on the correct device,
then no copy is performed and the original object is returned.

Args:
    device (:class:`torch.device`): The destination GPU device.
        Defaults to the current CUDA device.
    non_blocking (bool): If ``True`` and the source is in pinned memory,
        the copy will be asynchronous with respect to the host.
        Otherwise, the argument has no effect. Default: ``False``.
    {memory_format}
""".format(**common_args))

add_docstr_all('logcumsumexp',
               r"""
logcumsumexp(dim) -> Tensor

See :func:`torch.logcumsumexp`
""")

add_docstr_all('cummax',
               r"""
cummax(dim) -> (Tensor, Tensor)

See :func:`torch.cummax`
""")

add_docstr_all('cummin',
               r"""
cummin(dim) -> (Tensor, Tensor)

See :func:`torch.cummin`
""")

add_docstr_all('cumprod',
               r"""
cumprod(dim, dtype=None) -> Tensor

See :func:`torch.cumprod`
""")

add_docstr_all('cumsum',
               r"""
cumsum(dim, dtype=None) -> Tensor

See :func:`torch.cumsum`
""")

add_docstr_all('data_ptr',
               r"""
data_ptr() -> int

Returns the address of the first element of :attr:`self` tensor.
""")

add_docstr_all('dequantize',
               r"""
dequantize() -> Tensor

Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
""")

add_docstr_all('dense_dim',
               r"""
dense_dim() -> int

If :attr:`self` is a sparse COO tensor (i.e., with ``torch.sparse_coo`` layout),
this returns the number of dense dimensions. Otherwise, this throws an error.

See also :meth:`Tensor.sparse_dim`.
""")

add_docstr_all('diag',
               r"""
diag(diagonal=0) -> Tensor

See :func:`torch.diag`
""")

add_docstr_all('diag_embed',
               r"""
diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor

See :func:`torch.diag_embed`
""")

add_docstr_all('diagflat',
               r"""
diagflat(offset=0) -> Tensor

See :func:`torch.diagflat`
""")

add_docstr_all('diagonal',
               r"""
diagonal(offset=0, dim1=0, dim2=1) -> Tensor

See :func:`torch.diagonal`
""")

add_docstr_all('fill_diagonal_',
               r"""
fill_diagonal_(fill_value, wrap=False) -> Tensor

Fill the main diagonal of a tensor that has at least 2-dimensions.
When dims>2, all dimensions of input must be of equal length.
This function modifies the input tensor in-place, and returns the input tensor.

Arguments:
    fill_value (Scalar): the fill value
    wrap (bool): the diagonal 'wrapped' after N columns for tall matrices.

Example::

    >>> a = torch.zeros(3, 3)
    >>> a.fill_diagonal_(5)
    tensor([[5., 0., 0.],
            [0., 5., 0.],
            [0., 0., 5.]])
    >>> b = torch.zeros(7, 3)
    >>> b.fill_diagonal_(5)
    tensor([[5., 0., 0.],
            [0., 5., 0.],
            [0., 0., 5.],
            [0., 0., 0.],
            [0., 0., 0.],
            [0., 0., 0.],
            [0., 0., 0.]])
    >>> c = torch.zeros(7, 3)
    >>> c.fill_diagonal_(5, wrap=True)
    tensor([[5., 0., 0.],
            [0., 5., 0.],
            [0., 0., 5.],
            [0., 0., 0.],
            [5., 0., 0.],
            [0., 5., 0.],
            [0., 0., 5.]])

""")

add_docstr_all('floor_divide',
               r"""
floor_divide(value) -> Tensor

See :func:`torch.floor_divide`
""")

add_docstr_all('floor_divide_',
               r"""
floor_divide_(value) -> Tensor

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

add_docstr_all('digamma',
               r"""
digamma() -> Tensor

See :func:`torch.digamma`
""")

add_docstr_all('digamma_',
               r"""
digamma_() -> Tensor

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

add_docstr_all('dim',
               r"""
dim() -> int

Returns the number of dimensions of :attr:`self` tensor.
""")

add_docstr_all('dist',
               r"""
dist(other, p=2) -> Tensor

See :func:`torch.dist`
""")

add_docstr_all('div',
               r"""
div(value) -> Tensor

See :func:`torch.div`
""")

add_docstr_all('div_',
               r"""
div_(value) -> Tensor

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

add_docstr_all('dot',
               r"""
dot(tensor2) -> Tensor

See :func:`torch.dot`
""")

add_docstr_all('eig',
               r"""
eig(eigenvectors=False) -> (Tensor, Tensor)

See :func:`torch.eig`
""")

add_docstr_all('element_size',
               r"""
element_size() -> int

Returns the size in bytes of an individual element.

Example::

    >>> torch.tensor([]).element_size()
    4
    >>> torch.tensor([], dtype=torch.uint8).element_size()
    1

""")

add_docstr_all('eq',
               r"""
eq(other) -> Tensor

See :func:`torch.eq`
""")

add_docstr_all('eq_',
               r"""
eq_(other) -> Tensor

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

add_docstr_all('equal',
               r"""
equal(other) -> bool

See :func:`torch.equal`
""")

add_docstr_all('erf',
               r"""
erf() -> Tensor

See :func:`torch.erf`
""")

add_docstr_all('erf_',
               r"""
erf_() -> Tensor

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

add_docstr_all('erfc',
               r"""
erfc() -> Tensor

See :func:`torch.erfc`
""")

add_docstr_all('erfc_',
               r"""
erfc_() -> Tensor

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

add_docstr_all('erfinv',
               r"""
erfinv() -> Tensor

See :func:`torch.erfinv`
""")

add_docstr_all('erfinv_',
               r"""
erfinv_() -> Tensor

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

add_docstr_all('exp',
               r"""
exp() -> Tensor

See :func:`torch.exp`
""")

add_docstr_all('exp_',
               r"""
exp_() -> Tensor

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

add_docstr_all('expm1',
               r"""
expm1() -> Tensor

See :func:`torch.expm1`
""")

add_docstr_all('expm1_',
               r"""
expm1_() -> Tensor

In-place version of :meth:`~Tensor.expm1`
""")

add_docstr_all('exponential_',
               r"""
exponential_(lambd=1, *, generator=None) -> Tensor

Fills :attr:`self` tensor with elements drawn from the exponential distribution:

.. math::

    f(x) = \lambda e^{-\lambda x}
""")

add_docstr_all('fill_',
               r"""
fill_(value) -> Tensor

Fills :attr:`self` tensor with the specified value.
""")

add_docstr_all('floor',
               r"""
floor() -> Tensor

See :func:`torch.floor`
""")

add_docstr_all('flip',
               r"""
flip(dims) -> Tensor

See :func:`torch.flip`
""")

add_docstr_all('fliplr',
               r"""
fliplr() -> Tensor

See :func:`torch.fliplr`
""")

add_docstr_all('flipud',
               r"""
flipud() -> Tensor

See :func:`torch.flipud`
""")

add_docstr_all('roll',
               r"""
roll(shifts, dims) -> Tensor

See :func:`torch.roll`
""")

add_docstr_all('floor_',
               r"""
floor_() -> Tensor

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

add_docstr_all('fmod',
               r"""
fmod(divisor) -> Tensor

See :func:`torch.fmod`
""")

add_docstr_all('fmod_',
               r"""
fmod_(divisor) -> Tensor

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

add_docstr_all('frac',
               r"""
frac() -> Tensor

See :func:`torch.frac`
""")

add_docstr_all('frac_',
               r"""
frac_() -> Tensor

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

add_docstr_all('flatten',
               r"""
flatten(input, start_dim=0, end_dim=-1) -> Tensor

see :func:`torch.flatten`
""")

add_docstr_all('gather',
               r"""
gather(dim, index) -> Tensor

See :func:`torch.gather`
""")

add_docstr_all('gcd',
               r"""
gcd(other) -> Tensor

See :func:`torch.gcd`
""")

add_docstr_all('gcd_',
               r"""
gcd_(other) -> Tensor

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

add_docstr_all('ge',
               r"""
ge(other) -> Tensor

See :func:`torch.ge`
""")

add_docstr_all('ge_',
               r"""
ge_(other) -> Tensor

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

add_docstr_all('geometric_',
               r"""
geometric_(p, *, generator=None) -> Tensor

Fills :attr:`self` tensor with elements drawn from the geometric distribution:

.. math::

    f(X=k) = p^{k - 1} (1 - p)

""")

add_docstr_all('geqrf',
               r"""
geqrf() -> (Tensor, Tensor)

See :func:`torch.geqrf`
""")

add_docstr_all('ger',
               r"""
ger(vec2) -> Tensor

See :func:`torch.ger`
""")

add_docstr_all('indices',
               r"""
indices() -> Tensor

If :attr:`self` is a sparse COO tensor (i.e., with ``torch.sparse_coo`` layout),
this returns a view of the contained indices tensor. Otherwise, this throws an
error.

See also :meth:`Tensor.values`.

.. note::
  This method can only be called on a coalesced sparse tensor. See
  :meth:`Tensor.coalesce` for details.
""")

add_docstr_all('get_device',
               r"""
get_device() -> Device ordinal (Integer)

For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides.
For CPU tensors, an error is thrown.

Example::

    >>> x = torch.randn(3, 4, 5, device='cuda:0')
    >>> x.get_device()
    0
    >>> x.cpu().get_device()  # RuntimeError: get_device is not implemented for type torch.FloatTensor
""")

add_docstr_all('values',
               r"""
values() -> Tensor

If :attr:`self` is a sparse COO tensor (i.e., with ``torch.sparse_coo`` layout),
this returns a view of the contained values tensor. Otherwise, this throws an
error.

See also :meth:`Tensor.indices`.

.. note::
  This method can only be called on a coalesced sparse tensor. See
  :meth:`Tensor.coalesce` for details.
""")

add_docstr_all('gt',
               r"""
gt(other) -> Tensor

See :func:`torch.gt`
""")

add_docstr_all('gt_',
               r"""
gt_(other) -> Tensor

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

add_docstr_all('has_names',
               r"""
Is ``True`` if any of this tensor's dimensions are named. Otherwise, is ``False``.
""")

add_docstr_all('hardshrink',
               r"""
hardshrink(lambd=0.5) -> Tensor

See :func:`torch.nn.functional.hardshrink`
""")

add_docstr_all('histc',
               r"""
histc(bins=100, min=0, max=0) -> Tensor

See :func:`torch.histc`
""")

add_docstr_all('index_add_',
               r"""
index_add_(dim, index, tensor) -> Tensor

Accumulate the elements of :attr:`tensor` into the :attr:`self` tensor by adding
to the indices in the order given in :attr:`index`. For example, if ``dim == 0``
and ``index[i] == j``, then the ``i``\ th row of :attr:`tensor` is added to the
``j``\ th row of :attr:`self`.

The :attr:`dim`\ th dimension of :attr:`tensor` must have the same size as the
length of :attr:`index` (which must be a vector), and all other dimensions must
match :attr:`self`, or an error will be raised.

Note:
    In some circumstances when using the CUDA backend with CuDNN, this operator
    may select a nondeterministic algorithm to increase performance. If this is
    undesirable, you can try to make the operation deterministic (potentially at
    a performance cost) by setting ``torch.backends.cudnn.deterministic =
    True``.
    Please see the notes on :doc:`/notes/randomness` for background.

Args:
    dim (int): dimension along which to index
    index (LongTensor): indices of :attr:`tensor` to select from
    tensor (Tensor): the tensor containing values to add

Example::

    >>> x = torch.ones(5, 3)
    >>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float)
    >>> index = torch.tensor([0, 4, 2])
    >>> x.index_add_(0, index, t)
    tensor([[  2.,   3.,   4.],
            [  1.,   1.,   1.],
            [  8.,   9.,  10.],
            [  1.,   1.,   1.],
            [  5.,   6.,   7.]])
""")

add_docstr_all('index_copy_',
               r"""
index_copy_(dim, index, tensor) -> Tensor

Copies the elements of :attr:`tensor` into the :attr:`self` tensor by selecting
the indices in the order given in :attr:`index`. For example, if ``dim == 0``
and ``index[i] == j``, then the ``i``\ th row of :attr:`tensor` is copied to the
``j``\ th row of :attr:`self`.

The :attr:`dim`\ th dimension of :attr:`tensor` must have the same size as the
length of :attr:`index` (which must be a vector), and all other dimensions must
match :attr:`self`, or an error will be raised.

Args:
    dim (int): dimension along which to index
    index (LongTensor): indices of :attr:`tensor` to select from
    tensor (Tensor): the tensor containing values to copy

Example::

    >>> x = torch.zeros(5, 3)
    >>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float)
    >>> index = torch.tensor([0, 4, 2])
    >>> x.index_copy_(0, index, t)
    tensor([[ 1.,  2.,  3.],
            [ 0.,  0.,  0.],
            [ 7.,  8.,  9.],
            [ 0.,  0.,  0.],
            [ 4.,  5.,  6.]])
""")

add_docstr_all('index_fill_',
               r"""
index_fill_(dim, index, val) -> Tensor

Fills the elements of the :attr:`self` tensor with value :attr:`val` by
selecting the indices in the order given in :attr:`index`.

Args:
    dim (int): dimension along which to index
    index (LongTensor): indices of :attr:`self` tensor to fill in
    val (float): the value to fill with

Example::
    >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float)
    >>> index = torch.tensor([0, 2])
    >>> x.index_fill_(1, index, -1)
    tensor([[-1.,  2., -1.],
            [-1.,  5., -1.],
            [-1.,  8., -1.]])
""")

add_docstr_all('index_put_',
               r"""
index_put_(indices, value, accumulate=False) -> Tensor

Puts values from the tensor :attr:`value` into the tensor :attr:`self` using
the indices specified in :attr:`indices` (which is a tuple of Tensors). The
expression ``tensor.index_put_(indices, value)`` is equivalent to
``tensor[indices] = value``. Returns :attr:`self`.

If :attr:`accumulate` is ``True``, the elements in :attr:`value` are added to
:attr:`self`. If accumulate is ``False``, the behavior is undefined if indices
contain duplicate elements.

Args:
    indices (tuple of LongTensor): tensors used to index into `self`.
    value (Tensor): tensor of same dtype as `self`.
    accumulate (bool): whether to accumulate into self
""")

add_docstr_all('index_put',
               r"""
index_put(tensor1, indices, value, accumulate=False) -> Tensor

Out-place version of :meth:`~Tensor.index_put_`.
`tensor1` corresponds to `self` in :meth:`torch.Tensor.index_put_`.
""")

add_docstr_all('index_select',
               r"""
index_select(dim, index) -> Tensor

See :func:`torch.index_select`
""")

add_docstr_all('sparse_mask',
               r"""
sparse_mask(input, mask) -> Tensor

Returns a new SparseTensor with values from Tensor :attr:`input` filtered
by indices of :attr:`mask` and values are ignored. :attr:`input` and :attr:`mask`
must have the same shape.

Args:
    input (Tensor): an input Tensor
    mask (SparseTensor): a SparseTensor which we filter :attr:`input` based on its indices

Example::

    >>> nnz = 5
    >>> dims = [5, 5, 2, 2]
    >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
                       torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
    >>> V = torch.randn(nnz, dims[2], dims[3])
    >>> size = torch.Size(dims)
    >>> S = torch.sparse_coo_tensor(I, V, size).coalesce()
    >>> D = torch.randn(dims)
    >>> D.sparse_mask(S)
    tensor(indices=tensor([[0, 0, 0, 2],
                           [0, 1, 4, 3]]),
           values=tensor([[[ 1.6550,  0.2397],
                           [-0.1611, -0.0779]],

                          [[ 0.2326, -1.0558],
                           [ 1.4711,  1.9678]],

                          [[-0.5138, -0.0411],
                           [ 1.9417,  0.5158]],

                          [[ 0.0793,  0.0036],
                           [-0.2569, -0.1055]]]),
           size=(5, 5, 2, 2), nnz=4, layout=torch.sparse_coo)
""")

add_docstr_all('inverse',
               r"""
inverse() -> Tensor

See :func:`torch.inverse`
""")

add_docstr_all('isnan',
               r"""
isnan() -> Tensor

See :func:`torch.isnan`
""")

add_docstr_all('isinf',
               r"""
isinf() -> Tensor

See :func:`torch.isinf`
""")

add_docstr_all('isfinite',
               r"""
isfinite() -> Tensor

See :func:`torch.isfinite`
""")

add_docstr_all('isclose',
               r"""
isclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor

See :func:`torch.isclose`
""")

add_docstr_all('isreal',
               r"""
isreal() -> Tensor

See :func:`torch.isreal`
""")

add_docstr_all('is_contiguous',
               r"""
is_contiguous(memory_format=torch.contiguous_format) -> bool

Returns True if :attr:`self` tensor is contiguous in memory in the order specified
by memory format.

Args:
    memory_format (:class:`torch.memory_format`, optional): Specifies memory allocation
        order. Default: ``torch.contiguous_format``.
""")

add_docstr_all('is_pinned',
               r"""
Returns true if this tensor resides in pinned memory.
""")

add_docstr_all('is_floating_point',
               r"""
is_floating_point() -> bool

Returns True if the data type of :attr:`self` is a floating point data type.
""")

add_docstr_all('is_complex',
               r"""
is_complex() -> bool

Returns True if the data type of :attr:`self` is a complex data type.
""")

add_docstr_all('is_signed',
               r"""
is_signed() -> bool

Returns True if the data type of :attr:`self` is a signed data type.
""")

add_docstr_all('is_set_to',
               r"""
is_set_to(tensor) -> bool

Returns True if this object refers to the same ``THTensor`` object from the
Torch C API as the given tensor.
""")

add_docstr_all('item', r"""
item() -> number

Returns the value of this tensor as a standard Python number. This only works
for tensors with one element. For other cases, see :meth:`~Tensor.tolist`.

This operation is not differentiable.

Example::

    >>> x = torch.tensor([1.0])
    >>> x.item()
    1.0

""")

add_docstr_all('kthvalue',
               r"""
kthvalue(k, dim=None, keepdim=False) -> (Tensor, LongTensor)

See :func:`torch.kthvalue`
""")

add_docstr_all('lcm',
               r"""
lcm(other) -> Tensor

See :func:`torch.lcm`
""")

add_docstr_all('lcm_',
               r"""
lcm_(other) -> Tensor

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

add_docstr_all('le',
               r"""
le(other) -> Tensor

See :func:`torch.le`
""")

add_docstr_all('le_',
               r"""
le_(other) -> Tensor

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

add_docstr_all('lerp',
               r"""
lerp(end, weight) -> Tensor

See :func:`torch.lerp`
""")

add_docstr_all('lerp_',
               r"""
lerp_(end, weight) -> Tensor

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

add_docstr_all('lgamma',
               r"""
lgamma() -> Tensor

See :func:`torch.lgamma`
""")

add_docstr_all('lgamma_', r"""
lgamma_() -> Tensor

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

add_docstr_all('log',
               r"""
log() -> Tensor

See :func:`torch.log`
""")

add_docstr_all('log_', r"""
log_() -> Tensor

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

add_docstr_all('log10',
               r"""
log10() -> Tensor

See :func:`torch.log10`
""")

add_docstr_all('log10_',
               r"""
log10_() -> Tensor

In-place version of :meth:`~Tensor.log10`
""")

add_docstr_all('log1p',
               r"""
log1p() -> Tensor

See :func:`torch.log1p`
""")

add_docstr_all('log1p_',
               r"""
log1p_() -> Tensor

In-place version of :meth:`~Tensor.log1p`
""")

add_docstr_all('log2',
               r"""
log2() -> Tensor

See :func:`torch.log2`
""")

add_docstr_all('log2_',
               r"""
log2_() -> Tensor

In-place version of :meth:`~Tensor.log2`
""")

add_docstr_all('logaddexp',
               r"""
logaddexp(other) -> Tensor

See :func:`torch.logaddexp`
""")

add_docstr_all('logaddexp2',
               r"""
logaddexp2(other) -> Tensor

See :func:`torch.logaddexp2`
""")

add_docstr_all('log_normal_', r"""
log_normal_(mean=1, std=2, *, generator=None)

Fills :attr:`self` tensor with numbers samples from the log-normal distribution
parameterized by the given mean :math:`\mu` and standard deviation
:math:`\sigma`. Note that :attr:`mean` and :attr:`std` are the mean and
standard deviation of the underlying normal distribution, and not of the
returned distribution:

.. math::

    f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
""")

add_docstr_all('logsumexp',
               r"""
logsumexp(dim, keepdim=False) -> Tensor

See :func:`torch.logsumexp`
""")

add_docstr_all('lstsq',
               r"""
lstsq(A) -> (Tensor, Tensor)

See :func:`torch.lstsq`
""")

add_docstr_all('lt',
               r"""
lt(other) -> Tensor

See :func:`torch.lt`
""")

add_docstr_all('lt_',
               r"""
lt_(other) -> Tensor

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

add_docstr_all('lu_solve',
               r"""
lu_solve(LU_data, LU_pivots) -> Tensor

See :func:`torch.lu_solve`
""")

add_docstr_all('map_',
               r"""
map_(tensor, callable)

Applies :attr:`callable` for each element in :attr:`self` tensor and the given
:attr:`tensor` and stores the results in :attr:`self` tensor. :attr:`self` tensor and
the given :attr:`tensor` must be :ref:`broadcastable <broadcasting-semantics>`.

The :attr:`callable` should have the signature::

    def callable(a, b) -> number
""")

add_docstr_all('masked_scatter_',
               r"""
masked_scatter_(mask, source)

Copies elements from :attr:`source` into :attr:`self` tensor at positions where
the :attr:`mask` is True.
The shape of :attr:`mask` must be :ref:`broadcastable <broadcasting-semantics>`
with the shape of the underlying tensor. The :attr:`source` should have at least
as many elements as the number of ones in :attr:`mask`

Args:
    mask (BoolTensor): the boolean mask
    source (Tensor): the tensor to copy from

.. note::

    The :attr:`mask` operates on the :attr:`self` tensor, not on the given
    :attr:`source` tensor.
""")

add_docstr_all('masked_fill_',
               r"""
masked_fill_(mask, value)

Fills elements of :attr:`self` tensor with :attr:`value` where :attr:`mask` is
True. The shape of :attr:`mask` must be
:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
tensor.

Args:
    mask (BoolTensor): the boolean mask
    value (float): the value to fill in with
""")

add_docstr_all('masked_select',
               r"""
masked_select(mask) -> Tensor

See :func:`torch.masked_select`
""")

add_docstr_all('matrix_power',
               r"""
matrix_power(n) -> Tensor

See :func:`torch.matrix_power`
""")

add_docstr_all('max',
               r"""
max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)

See :func:`torch.max`
""")

add_docstr_all('argmax',
               r"""
argmax(dim=None, keepdim=False) -> LongTensor

See :func:`torch.argmax`
""")

add_docstr_all('mean',
               r"""
mean(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)

See :func:`torch.mean`
""")

add_docstr_all('median',
               r"""
median(dim=None, keepdim=False) -> (Tensor, LongTensor)

See :func:`torch.median`
""")

add_docstr_all('min',
               r"""
min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)

See :func:`torch.min`
""")

add_docstr_all('argmin',
               r"""
argmin(dim=None, keepdim=False) -> LongTensor

See :func:`torch.argmin`
""")

add_docstr_all('mm',
               r"""
mm(mat2) -> Tensor

See :func:`torch.mm`
""")

add_docstr_all('mode',
               r"""
mode(dim=None, keepdim=False) -> (Tensor, LongTensor)

See :func:`torch.mode`
""")

add_docstr_all('mul',
               r"""
mul(value) -> Tensor

See :func:`torch.mul`
""")

add_docstr_all('mul_',
               r"""
mul_(value)

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

add_docstr_all('multinomial',
               r"""
multinomial(num_samples, replacement=False, *, generator=None) -> Tensor

See :func:`torch.multinomial`
""")

add_docstr_all('mv',
               r"""
mv(vec) -> Tensor

See :func:`torch.mv`
""")

add_docstr_all('mvlgamma',
               r"""
mvlgamma(p) -> Tensor

See :func:`torch.mvlgamma`
""")

add_docstr_all('mvlgamma_',
               r"""
mvlgamma_(p) -> Tensor

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

add_docstr_all('narrow',
               r"""
narrow(dimension, start, length) -> Tensor

See :func:`torch.narrow`

Example::

    >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    >>> x.narrow(0, 0, 2)
    tensor([[ 1,  2,  3],
            [ 4,  5,  6]])
    >>> x.narrow(1, 1, 2)
    tensor([[ 2,  3],
            [ 5,  6],
            [ 8,  9]])
""")

add_docstr_all('narrow_copy',
               r"""
narrow_copy(dimension, start, length) -> Tensor

Same as :meth:`Tensor.narrow` except returning a copy rather
than shared storage.  This is primarily for sparse tensors, which
do not have a shared-storage narrow method.  Calling ```narrow_copy``
with ```dimemsion > self.sparse_dim()``` will return a copy with the
relevant dense dimension narrowed, and ```self.shape``` updated accordingly.
""")

add_docstr_all('ndimension',
               r"""
ndimension() -> int

Alias for :meth:`~Tensor.dim()`
""")

add_docstr_all('ne',
               r"""
ne(other) -> Tensor

See :func:`torch.ne`
""")

add_docstr_all('ne_',
               r"""
ne_(other) -> Tensor

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

add_docstr_all('neg',
               r"""
neg() -> Tensor

See :func:`torch.neg`
""")

add_docstr_all('neg_',
               r"""
neg_() -> Tensor

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

add_docstr_all('nelement',
               r"""
nelement() -> int

Alias for :meth:`~Tensor.numel`
""")

add_docstr_all('nonzero',
               r"""
nonzero() -> LongTensor

See :func:`torch.nonzero`
""")

add_docstr_all('norm',
               r"""
norm(p=2, dim=None, keepdim=False) -> Tensor

See :func:`torch.norm`
""")

add_docstr_all('normal_',
               r"""
normal_(mean=0, std=1, *, generator=None) -> Tensor

Fills :attr:`self` tensor with elements samples from the normal distribution
parameterized by :attr:`mean` and :attr:`std`.
""")

add_docstr_all('numel',
               r"""
numel() -> int

See :func:`torch.numel`
""")

add_docstr_all('numpy',
               r"""
numpy() -> numpy.ndarray

Returns :attr:`self` tensor as a NumPy :class:`ndarray`. This tensor and the
returned :class:`ndarray` share the same underlying storage. Changes to
:attr:`self` tensor will be reflected in the :class:`ndarray` and vice versa.
""")

add_docstr_all('orgqr',
               r"""
orgqr(input2) -> Tensor

See :func:`torch.orgqr`
""")

add_docstr_all('ormqr',
               r"""
ormqr(input2, input3, left=True, transpose=False) -> Tensor

See :func:`torch.ormqr`
""")


add_docstr_all('permute',
               r"""
permute(*dims) -> Tensor

Returns a view of the original tensor with its dimensions permuted.

Args:
    *dims (int...): The desired ordering of dimensions

Example:
    >>> x = torch.randn(2, 3, 5)
    >>> x.size()
    torch.Size([2, 3, 5])
    >>> x.permute(2, 0, 1).size()
    torch.Size([5, 2, 3])
""")

add_docstr_all('polygamma',
               r"""
polygamma(n) -> Tensor

See :func:`torch.polygamma`
""")

add_docstr_all('polygamma_',
               r"""
polygamma_(n) -> Tensor

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

add_docstr_all('pow',
               r"""
pow(exponent) -> Tensor

See :func:`torch.pow`
""")

add_docstr_all('pow_',
               r"""
pow_(exponent) -> Tensor

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

add_docstr_all('prod',
               r"""
prod(dim=None, keepdim=False, dtype=None) -> Tensor

See :func:`torch.prod`
""")

add_docstr_all('put_',
               r"""
put_(indices, tensor, accumulate=False) -> Tensor

Copies the elements from :attr:`tensor` into the positions specified by
indices. For the purpose of indexing, the :attr:`self` tensor is treated as if
it were a 1-D tensor.

If :attr:`accumulate` is ``True``, the elements in :attr:`tensor` are added to
:attr:`self`. If accumulate is ``False``, the behavior is undefined if indices
contain duplicate elements.

Args:
    indices (LongTensor): the indices into self
    tensor (Tensor): the tensor containing values to copy from
    accumulate (bool): whether to accumulate into self

Example::

    >>> src = torch.tensor([[4, 3, 5],
                            [6, 7, 8]])
    >>> src.put_(torch.tensor([1, 3]), torch.tensor([9, 10]))
    tensor([[  4,   9,   5],
            [ 10,   7,   8]])
""")

add_docstr_all('qr',
               r"""
qr(some=True) -> (Tensor, Tensor)

See :func:`torch.qr`
""")

add_docstr_all('qscheme',
               r"""
qscheme() -> torch.qscheme

Returns the quantization scheme of a given QTensor.
""")

add_docstr_all('q_scale',
               r"""
q_scale() -> float

Given a Tensor quantized by linear(affine) quantization,
returns the scale of the underlying quantizer().
""")

add_docstr_all('q_zero_point',
               r"""
q_zero_point() -> int

Given a Tensor quantized by linear(affine) quantization,
returns the zero_point of the underlying quantizer().
""")

add_docstr_all('q_per_channel_scales',
               r"""
q_per_channel_scales() -> Tensor

Given a Tensor quantized by linear (affine) per-channel quantization,
returns a Tensor of scales of the underlying quantizer. It has the number of
elements that matches the corresponding dimensions (from q_per_channel_axis) of
the tensor.
""")

add_docstr_all('q_per_channel_zero_points',
               r"""
q_per_channel_zero_points() -> Tensor

Given a Tensor quantized by linear (affine) per-channel quantization,
returns a tensor of zero_points of the underlying quantizer. It has the number of
elements that matches the corresponding dimensions (from q_per_channel_axis) of
the tensor.
""")

add_docstr_all('q_per_channel_axis',
               r"""
q_per_channel_axis() -> int

Given a Tensor quantized by linear (affine) per-channel quantization,
returns the index of dimension on which per-channel quantization is applied.
""")

add_docstr_all('random_',
               r"""
random_(from=0, to=None, *, generator=None) -> Tensor

Fills :attr:`self` tensor with numbers sampled from the discrete uniform
distribution over ``[from, to - 1]``. If not specified, the values are usually
only bounded by :attr:`self` tensor's data type. However, for floating point
types, if unspecified, range will be ``[0, 2^mantissa]`` to ensure that every
value is representable. For example, `torch.tensor(1, dtype=torch.double).random_()`
will be uniform in ``[0, 2^53]``.
""")

add_docstr_all('rad2deg',
               r"""
rad2deg() -> Tensor

See :func:`torch.rad2deg`
""")

add_docstr_all('rad2deg_',
               r"""
rad2deg_() -> Tensor

In-place version of :meth:`~Tensor.rad2deg`
""")

add_docstr_all('deg2rad',
               r"""
deg2rad() -> Tensor

See :func:`torch.deg2rad`
""")

add_docstr_all('deg2rad_',
               r"""
deg2rad_() -> Tensor

In-place version of :meth:`~Tensor.deg2rad`
""")

add_docstr_all('reciprocal',
               r"""
reciprocal() -> Tensor

See :func:`torch.reciprocal`
""")

add_docstr_all('reciprocal_',
               r"""
reciprocal_() -> Tensor

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

add_docstr_all('record_stream',
               r"""
record_stream(stream)

Ensures that the tensor memory is not reused for another tensor until all
current work queued on :attr:`stream` are complete.

.. note::

    The caching allocator is aware of only the stream where a tensor was
    allocated. Due to the awareness, it already correctly manages the life
    cycle of tensors on only one stream. But if a tensor is used on a stream
    different from the stream of origin, the allocator might reuse the memory
    unexpectedly. Calling this method lets the allocator know which streams
    have used the tensor.

""")

add_docstr_all('remainder',
               r"""
remainder(divisor) -> Tensor

See :func:`torch.remainder`
""")

add_docstr_all('remainder_',
               r"""
remainder_(divisor) -> Tensor

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

add_docstr_all('renorm',
               r"""
renorm(p, dim, maxnorm) -> Tensor

See :func:`torch.renorm`
""")

add_docstr_all('renorm_',
               r"""
renorm_(p, dim, maxnorm) -> Tensor

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

add_docstr_all('repeat',
               r"""
repeat(*sizes) -> Tensor

Repeats this tensor along the specified dimensions.

Unlike :meth:`~Tensor.expand`, this function copies the tensor's data.

.. warning::

    :meth:`~Tensor.repeat` behaves differently from
    `numpy.repeat <https://docs.scipy.org/doc/numpy/reference/generated/numpy.repeat.html>`_,
    but is more similar to
    `numpy.tile <https://docs.scipy.org/doc/numpy/reference/generated/numpy.tile.html>`_.
    For the operator similar to `numpy.repeat`, see :func:`torch.repeat_interleave`.

Args:
    sizes (torch.Size or int...): The number of times to repeat this tensor along each
        dimension

Example::

    >>> x = torch.tensor([1, 2, 3])
    >>> x.repeat(4, 2)
    tensor([[ 1,  2,  3,  1,  2,  3],
            [ 1,  2,  3,  1,  2,  3],
            [ 1,  2,  3,  1,  2,  3],
            [ 1,  2,  3,  1,  2,  3]])
    >>> x.repeat(4, 2, 1).size()
    torch.Size([4, 2, 3])
""")

add_docstr_all('repeat_interleave',
               r"""
repeat_interleave(repeats, dim=None) -> Tensor

See :func:`torch.repeat_interleave`.
""")

add_docstr_all('requires_grad_',
               r"""
requires_grad_(requires_grad=True) -> Tensor

Change if autograd should record operations on this tensor: sets this tensor's
:attr:`requires_grad` attribute in-place. Returns this tensor.

:func:`requires_grad_`'s main use case is to tell autograd to begin recording
operations on a Tensor ``tensor``. If ``tensor`` has ``requires_grad=False``
(because it was obtained through a DataLoader, or required preprocessing or
initialization), ``tensor.requires_grad_()`` makes it so that autograd will
begin to record operations on ``tensor``.

Args:
    requires_grad (bool): If autograd should record operations on this tensor.
        Default: ``True``.

Example::

    >>> # Let's say we want to preprocess some saved weights and use
    >>> # the result as new weights.
    >>> saved_weights = [0.1, 0.2, 0.3, 0.25]
    >>> loaded_weights = torch.tensor(saved_weights)
    >>> weights = preprocess(loaded_weights)  # some function
    >>> weights
    tensor([-0.5503,  0.4926, -2.1158, -0.8303])

    >>> # Now, start to record operations done to weights
    >>> weights.requires_grad_()
    >>> out = weights.pow(2).sum()
    >>> out.backward()
    >>> weights.grad
    tensor([-1.1007,  0.9853, -4.2316, -1.6606])

""")

add_docstr_all('reshape',
               r"""
reshape(*shape) -> Tensor

Returns a tensor with the same data and number of elements as :attr:`self`
but with the specified shape. This method returns a view if :attr:`shape` is
compatible with the current shape. See :meth:`torch.Tensor.view` on when it is
possible to return a view.

See :func:`torch.reshape`

Args:
    shape (tuple of ints or int...): the desired shape

""")

add_docstr_all('reshape_as',
               r"""
reshape_as(other) -> Tensor

Returns this tensor as the same shape as :attr:`other`.
``self.reshape_as(other)`` is equivalent to ``self.reshape(other.sizes())``.
This method returns a view if ``other.sizes()`` is compatible with the current
shape. See :meth:`torch.Tensor.view` on when it is possible to return a view.

Please see :meth:`reshape` for more information about ``reshape``.

Args:
    other (:class:`torch.Tensor`): The result tensor has the same shape
        as :attr:`other`.
""")

add_docstr_all('resize_',
               r"""
resize_(*sizes, memory_format=torch.contiguous_format) -> Tensor

Resizes :attr:`self` tensor to the specified size. If the number of elements is
larger than the current storage size, then the underlying storage is resized
to fit the new number of elements. If the number of elements is smaller, the
underlying storage is not changed. Existing elements are preserved but any new
memory is uninitialized.

.. warning::

    This is a low-level method. The storage is reinterpreted as C-contiguous,
    ignoring the current strides (unless the target size equals the current
    size, in which case the tensor is left unchanged). For most purposes, you
    will instead want to use :meth:`~Tensor.view()`, which checks for
    contiguity, or :meth:`~Tensor.reshape()`, which copies data if needed. To
    change the size in-place with custom strides, see :meth:`~Tensor.set_()`.

Args:
    sizes (torch.Size or int...): the desired size
    memory_format (:class:`torch.memory_format`, optional): the desired memory format of
        Tensor. Default: ``torch.contiguous_format``. Note that memory format of
        :attr:`self` is going to be unaffected if ``self.size()`` matches ``sizes``.

Example::

    >>> x = torch.tensor([[1, 2], [3, 4], [5, 6]])
    >>> x.resize_(2, 2)
    tensor([[ 1,  2],
            [ 3,  4]])
""")

add_docstr_all('resize_as_',
               r"""
resize_as_(tensor, memory_format=torch.contiguous_format) -> Tensor

Resizes the :attr:`self` tensor to be the same size as the specified
:attr:`tensor`. This is equivalent to ``self.resize_(tensor.size())``.

Args:
    memory_format (:class:`torch.memory_format`, optional): the desired memory format of
        Tensor. Default: ``torch.contiguous_format``. Note that memory format of
        :attr:`self` is going to be unaffected if ``self.size()`` matches ``tensor.size()``.

""")

add_docstr_all('rot90',
               r"""
rot90(k, dims) -> Tensor

See :func:`torch.rot90`
""")

add_docstr_all('round',
               r"""
round() -> Tensor

See :func:`torch.round`
""")

add_docstr_all('round_',
               r"""
round_() -> Tensor

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

add_docstr_all('rsqrt',
               r"""
rsqrt() -> Tensor

See :func:`torch.rsqrt`
""")

add_docstr_all('rsqrt_',
               r"""
rsqrt_() -> Tensor

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

add_docstr_all('scatter_',
               r"""
scatter_(dim, index, src, reduce=None) -> Tensor

Writes all values from the tensor :attr:`src` into :attr:`self` at the indices
specified in the :attr:`index` tensor. For each value in :attr:`src`, its output
index is specified by its index in :attr:`src` for ``dimension != dim`` and by
the corresponding value in :attr:`index` for ``dimension = dim``.

For a 3-D tensor, :attr:`self` is updated as::

    self[index[i][j][k]][j][k] = src[i][j][k]  # if dim == 0
    self[i][index[i][j][k]][k] = src[i][j][k]  # if dim == 1
    self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2

This is the reverse operation of the manner described in :meth:`~Tensor.gather`.

:attr:`self`, :attr:`index` and :attr:`src` (if it is a Tensor) should have same
number of dimensions. It is also required that ``index.size(d) <= src.size(d)``
for all dimensions ``d``, and that ``index.size(d) <= self.size(d)`` for all
dimensions ``d != dim``.

Moreover, as for :meth:`~Tensor.gather`, the values of :attr:`index` must be
between ``0`` and ``self.size(dim) - 1`` inclusive, and all values in a row
along the specified dimension :attr:`dim` must be unique.

Additionally accepts an optional :attr:`reduce` argument that allows
specification of an optional reduction operation, which is applied to all
values in the tensor :attr:`src` into :attr:`self` at the indicies
specified in the :attr:`index`. For each value in :attr:`src`, the reduction
operation is applied to an index in :attr:`self` which is specified by
its index in :attr:`src` for ``dimension != dim`` and by the corresponding
value in :attr:`index` for ``dimension = dim``.

Given a 3-D tensor and reduction using the multiplication operation, :attr:`self`
is updated as::

    self[index[i][j][k]][j][k] *= src[i][j][k]  # if dim == 0
    self[i][index[i][j][k]][k] *= src[i][j][k]  # if dim == 1
    self[i][j][index[i][j][k]] *= src[i][j][k]  # if dim == 2

Reducing with the addition operation is the same as using
:meth:`~torch.Tensor.scatter_add_`.

Note:
    Reduction is not yet implemented for the CUDA backend.

Args:
    dim (int): the axis along which to index
    index (LongTensor): the indices of elements to scatter,
      can be either empty or the same size of src.
      When empty, the operation returns identity
    src (Tensor): the source element(s) to scatter,
      incase `value` is not specified
    value (float): the source element(s) to scatter,
      incase `src` is not specified
    reduce (string): reduction operation to apply,
      can be either 'add', 'subtract', 'multiply' or 'divide'.

Example::

    >>> x = torch.rand(2, 5)
    >>> x
    tensor([[ 0.3992,  0.2908,  0.9044,  0.4850,  0.6004],
            [ 0.5735,  0.9006,  0.6797,  0.4152,  0.1732]])
    >>> torch.zeros(3, 5).scatter_(0, torch.tensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x)
    tensor([[ 0.3992,  0.9006,  0.6797,  0.4850,  0.6004],
            [ 0.0000,  0.2908,  0.0000,  0.4152,  0.0000],
            [ 0.5735,  0.0000,  0.9044,  0.0000,  0.1732]])

    >>> z = torch.zeros(2, 4).scatter_(1, torch.tensor([[2], [3]]), 1.23)
    >>> z
    tensor([[ 0.0000,  0.0000,  1.2300,  0.0000],
            [ 0.0000,  0.0000,  0.0000,  1.2300]])

    >>> z = torch.ones(2, 4).scatter_(1, torch.tensor([[2], [3]]), 1.23, reduce='multiply')
    >>> z
    tensor([[1.0000, 1.0000, 1.2300, 1.0000],
            [1.0000, 1.0000, 1.0000, 1.2300]])
""")

add_docstr_all('scatter_add_',
               r"""
scatter_add_(dim, index, src) -> Tensor

Adds all values from the tensor :attr:`other` into :attr:`self` at the indices
specified in the :attr:`index` tensor in a similar fashion as
:meth:`~torch.Tensor.scatter_`. For each value in :attr:`src`, it is added to
an index in :attr:`self` which is specified by its index in :attr:`src`
for ``dimension != dim`` and by the corresponding value in :attr:`index` for
``dimension = dim``.

For a 3-D tensor, :attr:`self` is updated as::

    self[index[i][j][k]][j][k] += src[i][j][k]  # if dim == 0
    self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1
    self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2

:attr:`self`, :attr:`index` and :attr:`src` should have same number of
dimensions. It is also required that ``index.size(d) <= src.size(d)`` for all
dimensions ``d``, and that ``index.size(d) <= self.size(d)`` for all dimensions
``d != dim``.

Note:
    In some circumstances when using the CUDA backend with CuDNN, this operator
    may select a nondeterministic algorithm to increase performance. If this is
    undesirable, you can try to make the operation deterministic (potentially at
    a performance cost) by setting ``torch.backends.cudnn.deterministic =
    True``.
    Please see the notes on :doc:`/notes/randomness` for background.

Args:
    dim (int): the axis along which to index
    index (LongTensor): the indices of elements to scatter and add,
      can be either empty or the same size of src.
      When empty, the operation returns identity.
    src (Tensor): the source elements to scatter and add

Example::

    >>> x = torch.rand(2, 5)
    >>> x
    tensor([[0.7404, 0.0427, 0.6480, 0.3806, 0.8328],
            [0.7953, 0.2009, 0.9154, 0.6782, 0.9620]])
    >>> torch.ones(3, 5).scatter_add_(0, torch.tensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x)
    tensor([[1.7404, 1.2009, 1.9154, 1.3806, 1.8328],
            [1.0000, 1.0427, 1.0000, 1.6782, 1.0000],
            [1.7953, 1.0000, 1.6480, 1.0000, 1.9620]])

""")

add_docstr_all('select',
               r"""
select(dim, index) -> Tensor

Slices the :attr:`self` tensor along the selected dimension at the given index.
This function returns a view of the original tensor with the given dimension removed.

Args:
    dim (int): the dimension to slice
    index (int): the index to select with

.. note::

    :meth:`select` is equivalent to slicing. For example,
    ``tensor.select(0, index)`` is equivalent to ``tensor[index]`` and
    ``tensor.select(2, index)`` is equivalent to ``tensor[:,:,index]``.
""")

add_docstr_all('set_',
               r"""
set_(source=None, storage_offset=0, size=None, stride=None) -> Tensor

Sets the underlying storage, size, and strides. If :attr:`source` is a tensor,
:attr:`self` tensor will share the same storage and have the same size and
strides as :attr:`source`. Changes to elements in one tensor will be reflected
in the other.

If :attr:`source` is a :class:`~torch.Storage`, the method sets the underlying
storage, offset, size, and stride.

Args:
    source (Tensor or Storage): the tensor or storage to use
    storage_offset (int, optional): the offset in the storage
    size (torch.Size, optional): the desired size. Defaults to the size of the source.
    stride (tuple, optional): the desired stride. Defaults to C-contiguous strides.
""")

add_docstr_all('sigmoid',
               r"""
sigmoid() -> Tensor

See :func:`torch.sigmoid`
""")

add_docstr_all('sigmoid_',
               r"""
sigmoid_() -> Tensor

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

add_docstr_all('logit',
               r"""
logit() -> Tensor

See :func:`torch.logit`
""")

add_docstr_all('logit_',
               r"""
logit_() -> Tensor

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

add_docstr_all('sign',
               r"""
sign() -> Tensor

See :func:`torch.sign`
""")

add_docstr_all('sign_',
               r"""
sign_() -> Tensor

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

add_docstr_all('sin',
               r"""
sin() -> Tensor

See :func:`torch.sin`
""")

add_docstr_all('sin_',
               r"""
sin_() -> Tensor

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

add_docstr_all('sinh',
               r"""
sinh() -> Tensor

See :func:`torch.sinh`
""")

add_docstr_all('sinh_',
               r"""
sinh_() -> Tensor

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

add_docstr_all('size',
               r"""
size() -> torch.Size

Returns the size of the :attr:`self` tensor. The returned value is a subclass of
:class:`tuple`.

Example::

    >>> torch.empty(3, 4, 5).size()
    torch.Size([3, 4, 5])

""")

add_docstr_all('solve',
               r"""
solve(A) -> Tensor, Tensor

See :func:`torch.solve`
""")

add_docstr_all('sort',
               r"""
sort(dim=-1, descending=False) -> (Tensor, LongTensor)

See :func:`torch.sort`
""")

add_docstr_all('argsort',
               r"""
argsort(dim=-1, descending=False) -> LongTensor

See :func:`torch.argsort`
""")

add_docstr_all('sparse_dim',
               r"""
sparse_dim() -> int

If :attr:`self` is a sparse COO tensor (i.e., with ``torch.sparse_coo`` layout),
this returns the number of sparse dimensions. Otherwise, this throws an error.

See also :meth:`Tensor.dense_dim`.
""")

add_docstr_all('sqrt',
               r"""
sqrt() -> Tensor

See :func:`torch.sqrt`
""")

add_docstr_all('sqrt_',
               r"""
sqrt_() -> Tensor

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

add_docstr_all('square',
               r"""
square() -> Tensor

See :func:`torch.square`
""")

add_docstr_all('square_',
               r"""
square_() -> Tensor

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

add_docstr_all('squeeze',
               r"""
squeeze(dim=None) -> Tensor

See :func:`torch.squeeze`
""")

add_docstr_all('squeeze_',
               r"""
squeeze_(dim=None) -> Tensor

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

add_docstr_all('std',
               r"""
std(dim=None, unbiased=True, keepdim=False) -> Tensor

See :func:`torch.std`
""")

add_docstr_all('storage',
               r"""
storage() -> torch.Storage

Returns the underlying storage.
""")

add_docstr_all('storage_offset',
               r"""
storage_offset() -> int

Returns :attr:`self` tensor's offset in the underlying storage in terms of
number of storage elements (not bytes).

Example::

    >>> x = torch.tensor([1, 2, 3, 4, 5])
    >>> x.storage_offset()
    0
    >>> x[3:].storage_offset()
    3

""")

add_docstr_all('storage_type',
               r"""
storage_type() -> type

Returns the type of the underlying storage.
""")

add_docstr_all('stride',
               r"""
stride(dim) -> tuple or int

Returns the stride of :attr:`self` tensor.

Stride is the jump necessary to go from one element to the next one in the
specified dimension :attr:`dim`. A tuple of all strides is returned when no
argument is passed in. Otherwise, an integer value is returned as the stride in
the particular dimension :attr:`dim`.

Args:
    dim (int, optional): the desired dimension in which stride is required

Example::

    >>> x = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
    >>> x.stride()
    (5, 1)
    >>>x.stride(0)
    5
    >>> x.stride(-1)
    1

""")

add_docstr_all('sub',
               r"""
sub(other, *, alpha=1) -> Tensor

Subtracts a scalar or tensor from :attr:`self` tensor. If both :attr:`alpha`
and :attr:`other` are specified, each element of :attr:`other` is scaled by
:attr:`alpha` before being used.

When :attr:`other` is a tensor, the shape of :attr:`other` must be
:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
tensor.

""")

add_docstr_all('sub_',
               r"""
sub_(other, *, alpha=1) -> Tensor

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

add_docstr_all('sum',
               r"""
sum(dim=None, keepdim=False, dtype=None) -> Tensor

See :func:`torch.sum`
""")

add_docstr_all('svd',
               r"""
svd(some=True, compute_uv=True) -> (Tensor, Tensor, Tensor)

See :func:`torch.svd`
""")

add_docstr_all('symeig',
               r"""
symeig(eigenvectors=False, upper=True) -> (Tensor, Tensor)

See :func:`torch.symeig`
""")

add_docstr_all('t',
               r"""
t() -> Tensor

See :func:`torch.t`
""")

add_docstr_all('t_',
               r"""
t_() -> Tensor

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

add_docstr_all('to',
               r"""
to(*args, **kwargs) -> Tensor

Performs Tensor dtype and/or device conversion. A :class:`torch.dtype` and :class:`torch.device` are
inferred from the arguments of ``self.to(*args, **kwargs)``.

.. note::

    If the ``self`` Tensor already
    has the correct :class:`torch.dtype` and :class:`torch.device`, then ``self`` is returned.
    Otherwise, the returned tensor is a copy of ``self`` with the desired
    :class:`torch.dtype` and :class:`torch.device`.

Here are the ways to call ``to``:

.. function:: to(dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor

    Returns a Tensor with the specified :attr:`dtype`

    Args:
        {memory_format}

.. function:: to(device=None, dtype=None, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor

    Returns a Tensor with the specified :attr:`device` and (optional)
    :attr:`dtype`. If :attr:`dtype` is ``None`` it is inferred to be ``self.dtype``.
    When :attr:`non_blocking`, tries to convert asynchronously with respect to
    the host if possible, e.g., converting a CPU Tensor with pinned memory to a
    CUDA Tensor.
    When :attr:`copy` is set, a new Tensor is created even when the Tensor
    already matches the desired conversion.

    Args:
        {memory_format}

.. function:: to(other, non_blocking=False, copy=False) -> Tensor

    Returns a Tensor with same :class:`torch.dtype` and :class:`torch.device` as
    the Tensor :attr:`other`. When :attr:`non_blocking`, tries to convert
    asynchronously with respect to the host if possible, e.g., converting a CPU
    Tensor with pinned memory to a CUDA Tensor.
    When :attr:`copy` is set, a new Tensor is created even when the Tensor
    already matches the desired conversion.

Example::

    >>> tensor = torch.randn(2, 2)  # Initially dtype=float32, device=cpu
    >>> tensor.to(torch.float64)
    tensor([[-0.5044,  0.0005],
            [ 0.3310, -0.0584]], dtype=torch.float64)

    >>> cuda0 = torch.device('cuda:0')
    >>> tensor.to(cuda0)
    tensor([[-0.5044,  0.0005],
            [ 0.3310, -0.0584]], device='cuda:0')

    >>> tensor.to(cuda0, dtype=torch.float64)
    tensor([[-0.5044,  0.0005],
            [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')

    >>> other = torch.randn((), dtype=torch.float64, device=cuda0)
    >>> tensor.to(other, non_blocking=True)
    tensor([[-0.5044,  0.0005],
            [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')
""".format(**common_args))

add_docstr_all('byte',
               r"""
byte(memory_format=torch.preserve_format) -> Tensor

``self.byte()`` is equivalent to ``self.to(torch.uint8)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('bool',
               r"""
bool(memory_format=torch.preserve_format) -> Tensor

``self.bool()`` is equivalent to ``self.to(torch.bool)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('char',
               r"""
char(memory_format=torch.preserve_format) -> Tensor

``self.char()`` is equivalent to ``self.to(torch.int8)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('bfloat16',
               r"""
bfloat16(memory_format=torch.preserve_format) -> Tensor
``self.bfloat16()`` is equivalent to ``self.to(torch.bfloat16)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('double',
               r"""
double(memory_format=torch.preserve_format) -> Tensor

``self.double()`` is equivalent to ``self.to(torch.float64)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('float',
               r"""
float(memory_format=torch.preserve_format) -> Tensor

``self.float()`` is equivalent to ``self.to(torch.float32)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('half',
               r"""
half(memory_format=torch.preserve_format) -> Tensor

``self.half()`` is equivalent to ``self.to(torch.float16)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('int',
               r"""
int(memory_format=torch.preserve_format) -> Tensor

``self.int()`` is equivalent to ``self.to(torch.int32)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('int_repr',
               r"""
int_repr() -> Tensor

Given a quantized Tensor,
``self.int_repr()`` returns a CPU Tensor with uint8_t as data type that stores the
underlying uint8_t values of the given Tensor.
""")


add_docstr_all('long',
               r"""
long(memory_format=torch.preserve_format) -> Tensor

``self.long()`` is equivalent to ``self.to(torch.int64)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('short',
               r"""
short(memory_format=torch.preserve_format) -> Tensor

``self.short()`` is equivalent to ``self.to(torch.int16)``. See :func:`to`.

Args:
    {memory_format}
""".format(**common_args))

add_docstr_all('take',
               r"""
take(indices) -> Tensor

See :func:`torch.take`
""")

add_docstr_all('tan',
               r"""
tan() -> Tensor

See :func:`torch.tan`
""")

add_docstr_all('tan_',
               r"""
tan_() -> Tensor

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

add_docstr_all('tanh',
               r"""
tanh() -> Tensor

See :func:`torch.tanh`
""")

add_docstr_all('tanh_',
               r"""
tanh_() -> Tensor

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

add_docstr_all('tolist',
               r"""
tolist() -> list or number

Returns the tensor as a (nested) list. For scalars, a standard
Python number is returned, just like with :meth:`~Tensor.item`.
Tensors are automatically moved to the CPU first if necessary.

This operation is not differentiable.

Examples::

    >>> a = torch.randn(2, 2)
    >>> a.tolist()
    [[0.012766935862600803, 0.5415473580360413],
     [-0.08909505605697632, 0.7729271650314331]]
    >>> a[0,0].tolist()
    0.012766935862600803
""")

add_docstr_all('topk',
               r"""
topk(k, dim=None, largest=True, sorted=True) -> (Tensor, LongTensor)

See :func:`torch.topk`
""")

add_docstr_all('to_sparse',
               r"""
to_sparse(sparseDims) -> Tensor
Returns a sparse copy of the tensor.  PyTorch supports sparse tensors in
:ref:`coordinate format <sparse-docs>`.

Args:
    sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor

Example::

    >>> d = torch.tensor([[0, 0, 0], [9, 0, 10], [0, 0, 0]])
    >>> d
    tensor([[ 0,  0,  0],
            [ 9,  0, 10],
            [ 0,  0,  0]])
    >>> d.to_sparse()
    tensor(indices=tensor([[1, 1],
                           [0, 2]]),
           values=tensor([ 9, 10]),
           size=(3, 3), nnz=2, layout=torch.sparse_coo)
    >>> d.to_sparse(1)
    tensor(indices=tensor([[1]]),
           values=tensor([[ 9,  0, 10]]),
           size=(3, 3), nnz=1, layout=torch.sparse_coo)
""")

add_docstr_all('to_mkldnn',
               r"""
to_mkldnn() -> Tensor
Returns a copy of the tensor in ``torch.mkldnn`` layout.

""")

add_docstr_all('trace',
               r"""
trace() -> Tensor

See :func:`torch.trace`
""")

add_docstr_all('transpose',
               r"""
transpose(dim0, dim1) -> Tensor

See :func:`torch.transpose`
""")

add_docstr_all('transpose_',
               r"""
transpose_(dim0, dim1) -> Tensor

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

add_docstr_all('triangular_solve',
               r"""
triangular_solve(A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor)

See :func:`torch.triangular_solve`
""")

add_docstr_all('tril',
               r"""
tril(k=0) -> Tensor

See :func:`torch.tril`
""")

add_docstr_all('tril_',
               r"""
tril_(k=0) -> Tensor

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

add_docstr_all('triu',
               r"""
triu(k=0) -> Tensor

See :func:`torch.triu`
""")

add_docstr_all('triu_',
               r"""
triu_(k=0) -> Tensor

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

add_docstr_all('true_divide',
               r"""
true_divide(value) -> Tensor

See :func:`torch.true_divide`
""")

add_docstr_all('true_divide_',
               r"""
true_divide_(value) -> Tensor

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

add_docstr_all('trunc',
               r"""
trunc() -> Tensor

See :func:`torch.trunc`
""")

add_docstr_all('trunc_',
               r"""
trunc_() -> Tensor

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

add_docstr_all('type',
               r"""
type(dtype=None, non_blocking=False, **kwargs) -> str or Tensor
Returns the type if `dtype` is not provided, else casts this object to
the specified type.

If this is already of the correct type, no copy is performed and the
original object is returned.

Args:
    dtype (type or string): The desired type
    non_blocking (bool): If ``True``, and the source is in pinned memory
        and destination is on the GPU or vice versa, the copy is performed
        asynchronously with respect to the host. Otherwise, the argument
        has no effect.
    **kwargs: For compatibility, may contain the key ``async`` in place of
        the ``non_blocking`` argument. The ``async`` arg is deprecated.
""")

add_docstr_all('type_as',
               r"""
type_as(tensor) -> Tensor

Returns this tensor cast to the type of the given tensor.

This is a no-op if the tensor is already of the correct type. This is
equivalent to ``self.type(tensor.type())``

Args:
    tensor (Tensor): the tensor which has the desired type
""")

add_docstr_all('unfold',
               r"""
unfold(dimension, size, step) -> Tensor

Returns a view of the original tensor which contains all slices of size :attr:`size` from
:attr:`self` tensor in the dimension :attr:`dimension`.

Step between two slices is given by :attr:`step`.

If `sizedim` is the size of dimension :attr:`dimension` for :attr:`self`, the size of
dimension :attr:`dimension` in the returned tensor will be
`(sizedim - size) / step + 1`.

An additional dimension of size :attr:`size` is appended in the returned tensor.

Args:
    dimension (int): dimension in which unfolding happens
    size (int): the size of each slice that is unfolded
    step (int): the step between each slice

Example::

    >>> x = torch.arange(1., 8)
    >>> x
    tensor([ 1.,  2.,  3.,  4.,  5.,  6.,  7.])
    >>> x.unfold(0, 2, 1)
    tensor([[ 1.,  2.],
            [ 2.,  3.],
            [ 3.,  4.],
            [ 4.,  5.],
            [ 5.,  6.],
            [ 6.,  7.]])
    >>> x.unfold(0, 2, 2)
    tensor([[ 1.,  2.],
            [ 3.,  4.],
            [ 5.,  6.]])
""")

add_docstr_all('uniform_',
               r"""
uniform_(from=0, to=1) -> Tensor

Fills :attr:`self` tensor with numbers sampled from the continuous uniform
distribution:

.. math::
    P(x) = \dfrac{1}{\text{to} - \text{from}}
""")

add_docstr_all('unsqueeze',
               r"""
unsqueeze(dim) -> Tensor

See :func:`torch.unsqueeze`
""")

add_docstr_all('unsqueeze_',
               r"""
unsqueeze_(dim) -> Tensor

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

add_docstr_all('var',
               r"""
var(dim=None, unbiased=True, keepdim=False) -> Tensor

See :func:`torch.var`
""")

add_docstr_all('view',
               r"""
view(*shape) -> Tensor

Returns a new tensor with the same data as the :attr:`self` tensor but of a
different :attr:`shape`.

The returned tensor shares the same data and must have the same number
of elements, but may have a different size. For a tensor to be viewed, the new
view size must be compatible with its original size and stride, i.e., each new
view dimension must either be a subspace of an original dimension, or only span
across original dimensions :math:`d, d+1, \dots, d+k` that satisfy the following
contiguity-like condition that :math:`\forall i = d, \dots, d+k-1`,

.. math::

  \text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]

Otherwise, it will not be possible to view :attr:`self` tensor as :attr:`shape`
without copying it (e.g., via :meth:`contiguous`). When it is unclear whether a
:meth:`view` can be performed, it is advisable to use :meth:`reshape`, which
returns a view if the shapes are compatible, and copies (equivalent to calling
:meth:`contiguous`) otherwise.

Args:
    shape (torch.Size or int...): the desired size

Example::

    >>> x = torch.randn(4, 4)
    >>> x.size()
    torch.Size([4, 4])
    >>> y = x.view(16)
    >>> y.size()
    torch.Size([16])
    >>> z = x.view(-1, 8)  # the size -1 is inferred from other dimensions
    >>> z.size()
    torch.Size([2, 8])

    >>> a = torch.randn(1, 2, 3, 4)
    >>> a.size()
    torch.Size([1, 2, 3, 4])
    >>> b = a.transpose(1, 2)  # Swaps 2nd and 3rd dimension
    >>> b.size()
    torch.Size([1, 3, 2, 4])
    >>> c = a.view(1, 3, 2, 4)  # Does not change tensor layout in memory
    >>> c.size()
    torch.Size([1, 3, 2, 4])
    >>> torch.equal(b, c)
    False

""")

add_docstr_all('view_as',
               r"""
view_as(other) -> Tensor

View this tensor as the same size as :attr:`other`.
``self.view_as(other)`` is equivalent to ``self.view(other.size())``.

Please see :meth:`~Tensor.view` for more information about ``view``.

Args:
    other (:class:`torch.Tensor`): The result tensor has the same size
        as :attr:`other`.
""")

add_docstr_all('expand',
               r"""
expand(*sizes) -> Tensor

Returns a new view of the :attr:`self` tensor with singleton dimensions expanded
to a larger size.

Passing -1 as the size for a dimension means not changing the size of
that dimension.

Tensor can be also expanded to a larger number of dimensions, and the
new ones will be appended at the front. For the new dimensions, the
size cannot be set to -1.

Expanding a tensor does not allocate new memory, but only creates a
new view on the existing tensor where a dimension of size one is
expanded to a larger size by setting the ``stride`` to 0. Any dimension
of size 1 can be expanded to an arbitrary value without allocating new
memory.

Args:
    *sizes (torch.Size or int...): the desired expanded size

.. warning::

    More than one element of an expanded tensor may refer to a single
    memory location. As a result, in-place operations (especially ones that
    are vectorized) may result in incorrect behavior. If you need to write
    to the tensors, please clone them first.

Example::

    >>> x = torch.tensor([[1], [2], [3]])
    >>> x.size()
    torch.Size([3, 1])
    >>> x.expand(3, 4)
    tensor([[ 1,  1,  1,  1],
            [ 2,  2,  2,  2],
            [ 3,  3,  3,  3]])
    >>> x.expand(-1, 4)   # -1 means not changing the size of that dimension
    tensor([[ 1,  1,  1,  1],
            [ 2,  2,  2,  2],
            [ 3,  3,  3,  3]])
""")

add_docstr_all('expand_as',
               r"""
expand_as(other) -> Tensor

Expand this tensor to the same size as :attr:`other`.
``self.expand_as(other)`` is equivalent to ``self.expand(other.size())``.

Please see :meth:`~Tensor.expand` for more information about ``expand``.

Args:
    other (:class:`torch.Tensor`): The result tensor has the same size
        as :attr:`other`.
""")

add_docstr_all('sum_to_size',
               r"""
sum_to_size(*size) -> Tensor

Sum ``this`` tensor to :attr:`size`.
:attr:`size` must be broadcastable to ``this`` tensor size.

Args:
    size (int...): a sequence of integers defining the shape of the output tensor.
""")


add_docstr_all('zero_',
               r"""
zero_() -> Tensor

Fills :attr:`self` tensor with zeros.
""")

add_docstr_all('matmul',
               r"""
matmul(tensor2) -> Tensor

See :func:`torch.matmul`
""")

add_docstr_all('chunk',
               r"""
chunk(chunks, dim=0) -> List of Tensors

See :func:`torch.chunk`
""")

add_docstr_all('stft',
               r"""
stft(frame_length, hop, fft_size=None, return_onesided=True, window=None, pad_end=0) -> Tensor

See :func:`torch.stft`
""")

add_docstr_all('istft',
               r"""
istft(n_fft, hop_length=None, win_length=None, window=None,
 center=True, normalized=False, onesided=True, length=None) -> Tensor

See :func:`torch.istft`
""")

add_docstr_all('fft',
               r"""
fft(signal_ndim, normalized=False) -> Tensor

See :func:`torch.fft`
""")

add_docstr_all('ifft',
               r"""
ifft(signal_ndim, normalized=False) -> Tensor

See :func:`torch.ifft`
""")

add_docstr_all('rfft',
               r"""
rfft(signal_ndim, normalized=False, onesided=True) -> Tensor

See :func:`torch.rfft`
""")

add_docstr_all('irfft',
               r"""
irfft(signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor

See :func:`torch.irfft`
""")

add_docstr_all('det',
               r"""
det() -> Tensor

See :func:`torch.det`
""")

add_docstr_all('where',
               r"""
where(condition, y) -> Tensor

``self.where(condition, y)`` is equivalent to ``torch.where(condition, self, y)``.
See :func:`torch.where`
""")

add_docstr_all('logdet',
               r"""
logdet() -> Tensor

See :func:`torch.logdet`
""")

add_docstr_all('slogdet',
               r"""
slogdet() -> (Tensor, Tensor)

See :func:`torch.slogdet`
""")

add_docstr_all('unbind',
               r"""
unbind(dim=0) -> seq

See :func:`torch.unbind`
""")

add_docstr_all('pin_memory',
               r"""
pin_memory() -> Tensor

Copies the tensor to pinned memory, if it's not already pinned.
""")

add_docstr_all('pinverse',
               r"""
pinverse() -> Tensor

See :func:`torch.pinverse`
""")

add_docstr_all('index_add',
               r"""
index_add(tensor1, dim, index, tensor2) -> Tensor

Out-of-place version of :meth:`torch.Tensor.index_add_`.
`tensor1` corresponds to `self` in :meth:`torch.Tensor.index_add_`.
""")

add_docstr_all('index_copy',
               r"""
index_copy(tensor1, dim, index, tensor2) -> Tensor

Out-of-place version of :meth:`torch.Tensor.index_copy_`.
`tensor1` corresponds to `self` in :meth:`torch.Tensor.index_copy_`.
""")

add_docstr_all('index_fill',
               r"""
index_fill(tensor1, dim, index, value) -> Tensor

Out-of-place version of :meth:`torch.Tensor.index_fill_`.
`tensor1` corresponds to `self` in :meth:`torch.Tensor.index_fill_`.
""")

add_docstr_all('scatter',
               r"""
scatter(dim, index, src) -> Tensor

Out-of-place version of :meth:`torch.Tensor.scatter_`
""")

add_docstr_all('scatter_add',
               r"""
scatter_add(dim, index, src) -> Tensor

Out-of-place version of :meth:`torch.Tensor.scatter_add_`
""")

add_docstr_all('masked_scatter',
               r"""
masked_scatter(mask, tensor) -> Tensor

Out-of-place version of :meth:`torch.Tensor.masked_scatter_`
""")

add_docstr_all('masked_fill',
               r"""
masked_fill(mask, value) -> Tensor

Out-of-place version of :meth:`torch.Tensor.masked_fill_`
""")

add_docstr_all('requires_grad',
               r"""
Is ``True`` if gradients need to be computed for this Tensor, ``False`` otherwise.

.. note::

    The fact that gradients need to be computed for a Tensor do not mean that the :attr:`grad`
    attribute will be populated, see :attr:`is_leaf` for more details.

""")

add_docstr_all('is_leaf',
               r"""
All Tensors that have :attr:`requires_grad` which is ``False`` will be leaf Tensors by convention.

For Tensors that have :attr:`requires_grad` which is ``True``, they will be leaf Tensors if they were
created by the user. This means that they are not the result of an operation and so
:attr:`grad_fn` is None.

Only leaf Tensors will have their :attr:`grad` populated during a call to :func:`backward`.
To get :attr:`grad` populated for non-leaf Tensors, you can use :func:`retain_grad`.

Example::

    >>> a = torch.rand(10, requires_grad=True)
    >>> a.is_leaf
    True
    >>> b = torch.rand(10, requires_grad=True).cuda()
    >>> b.is_leaf
    False
    # b was created by the operation that cast a cpu Tensor into a cuda Tensor
    >>> c = torch.rand(10, requires_grad=True) + 2
    >>> c.is_leaf
    False
    # c was created by the addition operation
    >>> d = torch.rand(10).cuda()
    >>> d.is_leaf
    True
    # d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)
    >>> e = torch.rand(10).cuda().requires_grad_()
    >>> e.is_leaf
    True
    # e requires gradients and has no operations creating it
    >>> f = torch.rand(10, requires_grad=True, device="cuda")
    >>> f.is_leaf
    True
    # f requires grad, has no operation creating it


""")

add_docstr_all('names',
               r"""
Stores names for each of this tensor's dimensions.

``names[idx]`` corresponds to the name of tensor dimension ``idx``.
Names are either a string if the dimension is named or ``None`` if the
dimension is unnamed.

Dimension names may contain characters or underscore. Furthermore, a dimension
name must be a valid Python variable name (i.e., does not start with underscore).

Tensors may not have two named dimensions with the same name.

.. warning::
    The named tensor API is experimental and subject to change.

""")

add_docstr_all('is_cuda',
               r"""
Is ``True`` if the Tensor is stored on the GPU, ``False`` otherwise.
""")

add_docstr_all('is_quantized',
               r"""
Is ``True`` if the Tensor is quantized, ``False`` otherwise.
""")

add_docstr_all('is_meta',
               r"""
Is ``True`` if the Tensor is a meta tensor, ``False`` otherwise.  Meta tensors
are like normal tensors, but they carry no data.
""")

add_docstr_all('device',
               r"""
Is the :class:`torch.device` where this Tensor is.
""")

add_docstr_all('ndim',
               r"""
Alias for :meth:`~Tensor.dim()`
""")

add_docstr_all('T',
               r"""
Is this Tensor with its dimensions reversed.

If ``n`` is the number of dimensions in ``x``,
``x.T`` is equivalent to ``x.permute(n-1, n-2, ..., 0)``.
""")

add_docstr_all('real',
               r"""
Returns a new tensor containing real values of the :attr:`self` tensor.
The returned tensor and :attr:`self` share the same underlying storage.

.. warning::
    :func:`real` is only supported for tensors with complex dtypes.

Example::
    >>> x=torch.randn(4, dtype=torch.cfloat)
    >>> x
    tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)])
    >>> x.real
    tensor([ 0.3100, -0.5445, -1.6492, -0.0638])

""")

add_docstr_all('imag',
               r"""
Returns a new tensor containing imaginary values of the :attr:`self` tensor.
The returned tensor and :attr:`self` share the same underlying storage.

.. warning::
    :func:`imag` is only supported for tensors with complex dtypes.

Example::
    >>> x=torch.randn(4, dtype=torch.cfloat)
    >>> x
    tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)])
    >>> x.imag
    tensor([ 0.3553, -0.7896, -0.0633, -0.8119])

""")

add_docstr_all('as_subclass',
               r"""
as_subclass(cls) -> Tensor

Makes a ``cls`` instance with the same data pointer as ``self``. Changes
in the output mirror changes in ``self``, and the output stays attached
to the autograd graph. ``cls`` must be a subclass of ``Tensor``.
""")