"""Adds docstrings to Tensor functions"""

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


tensor_classes = [
    'DoubleTensorBase',
    'FloatTensorBase',
    'LongTensorBase',
    'IntTensorBase',
    'ShortTensorBase',
    'CharTensorBase',
    'ByteTensorBase',
]


def add_docstr_all(method, docstr):
    for cls_name in tensor_classes:
        cls = getattr(torch._C, cls_name)
        try:
            add_docstr(getattr(cls, method), docstr)
        except AttributeError:
            pass


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('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('add',
               r"""
add(value)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Returns True if all elements in the tensor are non-zero, False otherwise.
""")

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

Returns True if any elements in the tensor are non-zero, False otherwise.
""")

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

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

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

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

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

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

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

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

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::

    P(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - 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('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() -> Tensor

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

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

Returns a contiguous tensor containing the same data as :attr:`self` tensor. If
:attr:`self` tensor is contiguous, this function returns the :attr:`self`
tensor.
""")

add_docstr_all('copy_',
               r"""
copy_(src, async=False, broadcast=True) -> Tensor

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

If :attr:`broadcast` is True, the :attr:`src` tensor must be
:ref:`broadcastable <broadcasting-semantics>` with :attr:`self` tensor.
Otherwise, :attr:`src` tensor should have the same number of elements as
: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
    async (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.
    broadcast (bool): if ``True``, :attr:`src` will be broadcast to the shape of
        the underlying tensor.
""")

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

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

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

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

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

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

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) -> float

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

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

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

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

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

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

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.FloatTensor().element_size()
    4
    >>> torch.ByteTensor().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('erfinv',
               r"""
erfinv() -> Tensor

See :func:`torch.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('exponential_',
               r"""
exponential_(lambd=1, *, generator=None) -> Tensor

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

.. math::

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

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

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('gels',
               r"""
gels(A) -> Tensor

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

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

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

.. math::

    P(X=k) = (1 - p)^{k - 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('gesv',
               r"""
gesv(A) -> Tensor, Tensor

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

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

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

add_docstr_all('index',
               r"""
index(m) -> Tensor

Selects elements from :attr:`self` tensor using a binary mask or along a given
dimension. The expression ``tensor.index(m)`` is equivalent to ``tensor[m]``.

Args:
    m (int or ByteTensor or slice): the dimension or mask used to select elements
""")

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.

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.Tensor(5, 3).fill_(1)
    >>> t = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    >>> index = torch.LongTensor([0, 4, 2])
    >>> x.index_add_(0, index, t)
    >>> x
      2   3   4
      1   1   1
      8   9  10
      1   1   1
      5   6   7
    [torch.FloatTensor of size 5x3]
""")

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]])
    >>> index = torch.LongTensor([0, 4, 2])
    >>> x.index_copy_(0, index, t)
    >>> x
     1  2  3
     0  0  0
     7  8  9
     0  0  0
     4  5  6
    [torch.FloatTensor of size 5x3]
""")

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]])
    >>> index = torch.LongTensor([0, 2])
    >>> x.index_fill_(1, index, -1)
    >>> x
    -1  2 -1
    -1  5 -1
    -1  8 -1
    [torch.FloatTensor of size 3x3]
""")

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

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

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

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

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

Returns True if :attr:`self` tensor is contiguous in memory in C order.
""")

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

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

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(start, end, weight)

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

add_docstr_all('lerp_',
               r"""
lerp_(start, end, weight)

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

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

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

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

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

add_docstr_all('log_normal_', u"""
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 (\u00B5) and standard deviation (\u03C3).
Note that :attr:`mean` and :attr:`stdv` are the mean and standard deviation of
the underlying normal distribution, and not of the returned distribution:

.. math::

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

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('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 one.
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 (ByteTensor): the binary 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
one. The shape of :attr:`mask` must be
:ref:`broadcastable <broadcasting-semantics>` with the shape of the underlying
tensor.

Args:
    mask (ByteTensor): the binary 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('max',
               r"""
max(dim=None, keepdim=False) -> float or (Tensor, Tensor)

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

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

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

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)

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

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

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

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

Returns a new tensor that is a narrowed version of :attr:`self` tensor. The
dimension :attr:`dim` is narrowed from :attr:`start` to :attr:`start + length`. The
returned tensor and :attr:`self` tensor share the same underlying storage.

Args:
    dimension (int): the dimension along which to narrow
    start (int): the starting dimension
    length (int): the distance to the ending dimension

Example:
    >>> x = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    >>> x.narrow(0, 0, 2)
     1  2  3
     4  5  6
    [torch.FloatTensor of size 2x3]
    >>> x.narrow(1, 1, 2)
     2  3
     5  6
     8  9
    [torch.FloatTensor of size 3x2]
""")

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) -> float

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

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

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() -> 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('potrf',
               r"""
potrf(upper=True) -> Tensor

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

add_docstr_all('potri',
               r"""
potri(upper=True) -> Tensor

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

add_docstr_all('potrs',
               r"""
potrs(input2, upper=True) -> Tensor

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

add_docstr_all('pow',
               r"""
pow(exponent)

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

add_docstr_all('pow_',
               r"""
pow_(exponent)

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

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

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

add_docstr_all('pstrf',
               r"""
pstrf(upper=True, tol=-1) -> (Tensor, IntTensor)

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

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 puropose 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
contains 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.LongTensor([1, 3]), torch.Tensor([9, 10]))
      4   9   5
     10   7   8
    [torch.FloatTensor of size 2x3]
""")

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

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

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

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.DoubleTensor(1).random_()` will be
uniform in ``[0, 2^53]``.
""")

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('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('resize_',
               r"""
resize_(*sizes)

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.

Args:
    sizes (torch.Size or int...): the desired size

Example:
    >>> x = torch.Tensor([[1, 2], [3, 4], [5, 6]])
    >>> x.resize_(2, 2)
    >>> x
     1  2
     3  4
    [torch.FloatTensor of size 2x2]
""")

add_docstr_all('resize_as_',
               r"""
resize_as_(tensor)

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

    self.resize_(tensor.size())
""")

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) -> 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 != :attr:`dim` and
by the corresponding value in :attr:`index` for dimension = :attr:`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` should have same number of
dimensions. It is also required that `index->size[d] <= src->size[d]` for all
dimension `d`, and that `index->size[d] <= real->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.

Args:
    input (Tensor): the source tensor
    dim (int): the axis along which to index
    index (LongTensor): the indices of elements to scatter
    src (Tensor or float): the source element(s) to scatter

Example::

    >>> x = torch.rand(2, 5)
    >>> x

     0.4319  0.6500  0.4080  0.8760  0.2355
     0.2609  0.4711  0.8486  0.8573  0.1029
    [torch.FloatTensor of size 2x5]

    >>> torch.zeros(3, 5).scatter_(0, torch.LongTensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x)

     0.4319  0.4711  0.8486  0.8760  0.2355
     0.0000  0.6500  0.0000  0.8573  0.0000
     0.2609  0.0000  0.4080  0.0000  0.1029
    [torch.FloatTensor of size 3x5]

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

     0.0000  0.0000  1.2300  0.0000
     0.0000  0.0000  0.0000  1.2300
    [torch.FloatTensor of size 2x4]

""")

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

Slices the :attr:`self` tensor along the selected dimension at the given index.
If :attr:`self` is one dimensional, this function returns a number. Otherwise,
it returns a 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)

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): the offset in the storage
    size (torch.Size): the desired size. Defaults to the size of the source.
    stride (tuple): 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('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.Tensor(3, 4, 5).size()
    torch.Size([3, 4, 5])
""")

add_docstr_all('sort',
               r"""
sort(dim=None, descending=False) -> (Tensor, LongTensor)

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

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('squeeze',
               r"""
squeeze(dim=None)

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

add_docstr_all('squeeze_',
               r"""
squeeze_(dim=None)

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

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

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('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(value, other) -> Tensor

Subtracts a scalar or tensor from :attr:`self` tensor. If both :attr:`value` and
:attr:`other` are specified, each element of :attr:`other` is scaled by
:attr:`value` 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_(x) -> Tensor

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

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

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

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

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

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

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

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

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('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('trtrs',
               r"""
trtrs(A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor)

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

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('unfold',
               r"""
unfold(dim, size, step) -> Tensor

Returns a tensor which contains all slices of size :attr:`size` from
:attr:`self` tensor in the dimension :attr:`dim`.

Step between two slices is given by :attr:`step`.

If `sizedim` is the size of dimension dim for :attr:`self`, the size of
dimension :attr:`dim` in the returned tensor will be
`(sizedim - size) / step + 1`.

An additional dimension of size size is appended in the returned tensor.

Args:
    dim (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

     1
     2
     3
     4
     5
     6
     7
    [torch.FloatTensor of size 7]

    >>> x.unfold(0, 2, 1)

     1  2
     2  3
     3  4
     4  5
     5  6
     6  7
    [torch.FloatTensor of size 6x2]

    >>> x.unfold(0, 2, 2)

     1  2
     3  4
     5  6
    [torch.FloatTensor of size 3x2]

""")

add_docstr_all('uniform_',
               r"""
uniform_(from=0, to=1) -> Tensor

Fills :attr:`self` tensor with numbers sampled from the uniform distribution:

.. math:

    P(x) = \dfrac{1}{to - from}
""")

add_docstr_all('unsqueeze',
               r"""
unsqueeze(dim)

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

add_docstr_all('unsqueeze_',
               r"""
unsqueeze_(dim)

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

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

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

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

Returns a new tensor with the same data as the :attr:`self` tensor but of a
different size.

The returned tensor shares the same data and must have the same number
of elements, but may have a different size. A tensor must be
:func:`contiguous` to be viewed.

Args:
    args (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])
""")

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

Example:
    >>> x = torch.Tensor([[1], [2], [3]])
    >>> x.size()
    torch.Size([3, 1])
    >>> x.expand(3, 4)
     1  1  1  1
     2  2  2  2
     3  3  3  3
    [torch.FloatTensor of size 3x4]
    >>> x.expand(-1, 4)   # -1 means not changing the size of that dimension
     1  1  1  1
     2  2  2  2
     3  3  3  3
    [torch.FloatTensor of size 3x4]
""")

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

Fills :attr:`self` tensor with zeros.
""")
