torch.Tensor A torch.Tensor is a multi-dimensional matrix containing elements of a single data type. Torch defines 10 tensor types with CPU and GPU variants which are as follows:   
Data type dtype CPU tensor GPU tensor   
32-bit floating point torch.float32 or torch.float torch.FloatTensor torch.cuda.FloatTensor  
64-bit floating point torch.float64 or torch.double torch.DoubleTensor torch.cuda.DoubleTensor  
16-bit floating point 1 torch.float16 or torch.half torch.HalfTensor torch.cuda.HalfTensor  
16-bit floating point 2 torch.bfloat16 torch.BFloat16Tensor torch.cuda.BFloat16Tensor  
32-bit complex torch.complex32    
64-bit complex torch.complex64    
128-bit complex torch.complex128 or torch.cdouble    
8-bit integer (unsigned) torch.uint8 torch.ByteTensor torch.cuda.ByteTensor  
8-bit integer (signed) torch.int8 torch.CharTensor torch.cuda.CharTensor  
16-bit integer (signed) torch.int16 or torch.short torch.ShortTensor torch.cuda.ShortTensor  
32-bit integer (signed) torch.int32 or torch.int torch.IntTensor torch.cuda.IntTensor  
64-bit integer (signed) torch.int64 or torch.long torch.LongTensor torch.cuda.LongTensor  
Boolean torch.bool torch.BoolTensor torch.cuda.BoolTensor    
1  
Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 significand bits. Useful when precision is important at the expense of range.  
2  
Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 significand bits. Useful when range is important, since it has the same number of exponent bits as float32   torch.Tensor is an alias for the default tensor type (torch.FloatTensor). A tensor can be constructed from a Python list or sequence using the torch.tensor() constructor: >>> torch.tensor([[1., -1.], [1., -1.]])
tensor([[ 1.0000, -1.0000],
        [ 1.0000, -1.0000]])
>>> torch.tensor(np.array([[1, 2, 3], [4, 5, 6]]))
tensor([[ 1,  2,  3],
        [ 4,  5,  6]])
  Warning torch.tensor() always copies data. If you have a Tensor data and just want to change its requires_grad flag, use requires_grad_() or detach() to avoid a copy. If you have a numpy array and want to avoid a copy, use torch.as_tensor().  A tensor of specific data type can be constructed by passing a torch.dtype and/or a torch.device to a constructor or tensor creation op: >>> torch.zeros([2, 4], dtype=torch.int32)
tensor([[ 0,  0,  0,  0],
        [ 0,  0,  0,  0]], dtype=torch.int32)
>>> cuda0 = torch.device('cuda:0')
>>> torch.ones([2, 4], dtype=torch.float64, device=cuda0)
tensor([[ 1.0000,  1.0000,  1.0000,  1.0000],
        [ 1.0000,  1.0000,  1.0000,  1.0000]], dtype=torch.float64, device='cuda:0')
 The contents of a tensor can be accessed and modified using Python’s indexing and slicing notation: >>> x = torch.tensor([[1, 2, 3], [4, 5, 6]])
>>> print(x[1][2])
tensor(6)
>>> x[0][1] = 8
>>> print(x)
tensor([[ 1,  8,  3],
        [ 4,  5,  6]])
 Use torch.Tensor.item() to get a Python number from a tensor containing a single value: >>> x = torch.tensor([[1]])
>>> x
tensor([[ 1]])
>>> x.item()
1
>>> x = torch.tensor(2.5)
>>> x
tensor(2.5000)
>>> x.item()
2.5
 A tensor can be created with requires_grad=True so that torch.autograd records operations on them for automatic differentiation. >>> x = torch.tensor([[1., -1.], [1., 1.]], requires_grad=True)
>>> out = x.pow(2).sum()
>>> out.backward()
>>> x.grad
tensor([[ 2.0000, -2.0000],
        [ 2.0000,  2.0000]])
 Each tensor has an associated torch.Storage, which holds its data. The tensor class also provides multi-dimensional, strided view of a storage and defines numeric operations on it.  Note For more information on tensor views, see Tensor Views.   Note For more information on the torch.dtype, torch.device, and torch.layout attributes of a torch.Tensor, see Tensor Attributes.   Note Methods which mutate a tensor are marked with an underscore suffix. For example, torch.FloatTensor.abs_() computes the absolute value in-place and returns the modified tensor, while torch.FloatTensor.abs() computes the result in a new tensor.   Note To change an existing tensor’s torch.device and/or torch.dtype, consider using to() method on the tensor.   Warning Current implementation of torch.Tensor introduces memory overhead, thus it might lead to unexpectedly high memory usage in the applications with many tiny tensors. If this is your case, consider using one large structure.   
class torch.Tensor  
There are a few main ways to create a tensor, depending on your use case.  To create a tensor with pre-existing data, use torch.tensor(). To create a tensor with specific size, use torch.* tensor creation ops (see Creation Ops). To create a tensor with the same size (and similar types) as another tensor, use torch.*_like tensor creation ops (see Creation Ops). To create a tensor with similar type but different size as another tensor, use tensor.new_* creation ops.   
new_tensor(data, dtype=None, device=None, requires_grad=False) → Tensor  
Returns a new Tensor with data as the tensor data. By default, the returned Tensor has the same torch.dtype and torch.device as this tensor.  Warning new_tensor() always copies data. If you have a Tensor data and want to avoid a copy, use torch.Tensor.requires_grad_() or torch.Tensor.detach(). If you have a numpy array and want to avoid a copy, use torch.from_numpy().   Warning When data is a tensor x, 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.   Parameters 
 
data (array_like) – The returned Tensor copies data. 
dtype (torch.dtype, optional) – the desired type of returned tensor. Default: if None, same torch.dtype as this tensor. 
device (torch.device, optional) – the desired device of returned tensor. Default: if None, same torch.device as this tensor. 
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.    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)
 
  
new_full(size, fill_value, dtype=None, device=None, requires_grad=False) → Tensor  
Returns a Tensor of size size filled with fill_value. By default, the returned Tensor has the same torch.dtype and torch.device as this tensor.  Parameters 
 
fill_value (scalar) – the number to fill the output tensor with. 
dtype (torch.dtype, optional) – the desired type of returned tensor. Default: if None, same torch.dtype as this tensor. 
device (torch.device, optional) – the desired device of returned tensor. Default: if None, same torch.device as this tensor. 
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.    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)
 
  
new_empty(size, dtype=None, device=None, requires_grad=False) → Tensor  
Returns a Tensor of size size filled with uninitialized data. By default, the returned Tensor has the same torch.dtype and torch.device as this tensor.  Parameters 
 
dtype (torch.dtype, optional) – the desired type of returned tensor. Default: if None, same torch.dtype as this tensor. 
device (torch.device, optional) – the desired device of returned tensor. Default: if None, same torch.device as this tensor. 
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.    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]])
 
  
new_ones(size, dtype=None, device=None, requires_grad=False) → Tensor  
Returns a Tensor of size size filled with 1. By default, the returned Tensor has the same torch.dtype and torch.device as this tensor.  Parameters 
 
size (int...) – a list, tuple, or torch.Size of integers defining the shape of the output tensor. 
dtype (torch.dtype, optional) – the desired type of returned tensor. Default: if None, same torch.dtype as this tensor. 
device (torch.device, optional) – the desired device of returned tensor. Default: if None, same torch.device as this tensor. 
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.    Example: >>> tensor = torch.tensor((), dtype=torch.int32)
>>> tensor.new_ones((2, 3))
tensor([[ 1,  1,  1],
        [ 1,  1,  1]], dtype=torch.int32)
 
  
new_zeros(size, dtype=None, device=None, requires_grad=False) → Tensor  
Returns a Tensor of size size filled with 0. By default, the returned Tensor has the same torch.dtype and torch.device as this tensor.  Parameters 
 
size (int...) – a list, tuple, or torch.Size of integers defining the shape of the output tensor. 
dtype (torch.dtype, optional) – the desired type of returned tensor. Default: if None, same torch.dtype as this tensor. 
device (torch.device, optional) – the desired device of returned tensor. Default: if None, same torch.device as this tensor. 
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.    Example: >>> tensor = torch.tensor((), dtype=torch.float64)
>>> tensor.new_zeros((2, 3))
tensor([[ 0.,  0.,  0.],
        [ 0.,  0.,  0.]], dtype=torch.float64)
 
  
is_cuda  
Is True if the Tensor is stored on the GPU, False otherwise. 
  
is_quantized  
Is True if the Tensor is quantized, False otherwise. 
  
is_meta  
Is True if the Tensor is a meta tensor, False otherwise. Meta tensors are like normal tensors, but they carry no data. 
  
device  
Is the torch.device where this Tensor is. 
  
grad  
This attribute is None by default and becomes a Tensor the first time a call to backward() computes gradients for self. The attribute will then contain the gradients computed and future calls to backward() will accumulate (add) gradients into it. 
  
ndim  
Alias for dim() 
  
T  
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). 
  
real  
Returns a new tensor containing real values of the self tensor. The returned tensor and self share the same underlying storage.  Warning 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])
   
  
imag  
Returns a new tensor containing imaginary values of the self tensor. The returned tensor and self share the same underlying storage.  Warning 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])
   
  
abs() → Tensor  
See torch.abs() 
  
abs_() → Tensor  
In-place version of abs() 
  
absolute() → Tensor  
Alias for abs() 
  
absolute_() → Tensor  
In-place version of absolute() Alias for abs_() 
  
acos() → Tensor  
See torch.acos() 
  
acos_() → Tensor  
In-place version of acos() 
  
arccos() → Tensor  
See torch.arccos() 
  
arccos_() → Tensor  
In-place version of arccos() 
  
add(other, *, alpha=1) → Tensor  
Add a scalar or tensor to self tensor. If both alpha and other are specified, each element of other is scaled by alpha before being used. When other is a tensor, the shape of other must be broadcastable with the shape of the underlying tensor See torch.add() 
  
add_(other, *, alpha=1) → Tensor  
In-place version of add() 
  
addbmm(batch1, batch2, *, beta=1, alpha=1) → Tensor  
See torch.addbmm() 
  
addbmm_(batch1, batch2, *, beta=1, alpha=1) → Tensor  
In-place version of addbmm() 
  
addcdiv(tensor1, tensor2, *, value=1) → Tensor  
See torch.addcdiv() 
  
addcdiv_(tensor1, tensor2, *, value=1) → Tensor  
In-place version of addcdiv() 
  
addcmul(tensor1, tensor2, *, value=1) → Tensor  
See torch.addcmul() 
  
addcmul_(tensor1, tensor2, *, value=1) → Tensor  
In-place version of addcmul() 
  
addmm(mat1, mat2, *, beta=1, alpha=1) → Tensor  
See torch.addmm() 
  
addmm_(mat1, mat2, *, beta=1, alpha=1) → Tensor  
In-place version of addmm() 
  
sspaddmm(mat1, mat2, *, beta=1, alpha=1) → Tensor  
See torch.sspaddmm() 
  
addmv(mat, vec, *, beta=1, alpha=1) → Tensor  
See torch.addmv() 
  
addmv_(mat, vec, *, beta=1, alpha=1) → Tensor  
In-place version of addmv() 
  
addr(vec1, vec2, *, beta=1, alpha=1) → Tensor  
See torch.addr() 
  
addr_(vec1, vec2, *, beta=1, alpha=1) → Tensor  
In-place version of addr() 
  
allclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) → Tensor  
See torch.allclose() 
  
amax(dim=None, keepdim=False) → Tensor  
See torch.amax() 
  
amin(dim=None, keepdim=False) → Tensor  
See torch.amin() 
  
angle() → Tensor  
See torch.angle() 
  
apply_(callable) → Tensor  
Applies the function callable to each element in the tensor, replacing each element with the value returned by callable.  Note This function only works with CPU tensors and should not be used in code sections that require high performance.  
  
argmax(dim=None, keepdim=False) → LongTensor  
See torch.argmax() 
  
argmin(dim=None, keepdim=False) → LongTensor  
See torch.argmin() 
  
argsort(dim=-1, descending=False) → LongTensor  
See torch.argsort() 
  
asin() → Tensor  
See torch.asin() 
  
asin_() → Tensor  
In-place version of asin() 
  
arcsin() → Tensor  
See torch.arcsin() 
  
arcsin_() → Tensor  
In-place version of arcsin() 
  
as_strided(size, stride, storage_offset=0) → Tensor  
See torch.as_strided() 
  
atan() → Tensor  
See torch.atan() 
  
atan_() → Tensor  
In-place version of atan() 
  
arctan() → Tensor  
See torch.arctan() 
  
arctan_() → Tensor  
In-place version of arctan() 
  
atan2(other) → Tensor  
See torch.atan2() 
  
atan2_(other) → Tensor  
In-place version of atan2() 
  
all(dim=None, keepdim=False) → Tensor  
See torch.all() 
  
any(dim=None, keepdim=False) → Tensor  
See torch.any() 
  
backward(gradient=None, retain_graph=None, create_graph=False, inputs=None) [source]
 
Computes the gradient of current tensor w.r.t. graph leaves. The graph is differentiated using the chain rule. If the tensor is non-scalar (i.e. its data has more than one element) and requires gradient, the function additionally requires specifying gradient. It should be a tensor of matching type and location, that contains the gradient of the differentiated function w.r.t. self. This function accumulates gradients in the leaves - you might need to zero .grad attributes or set them to None before calling it. See Default gradient layouts for details on the memory layout of accumulated gradients.  Note If you run any forward ops, create gradient, and/or call backward in a user-specified CUDA stream context, see Stream semantics of backward passes.   Parameters 
 
gradient (Tensor or None) – Gradient w.r.t. the tensor. If it is a tensor, it will be automatically converted to a Tensor that does not require grad unless create_graph is True. None values can be specified for scalar Tensors or ones that don’t require grad. If a None value would be acceptable then this argument is optional. 
retain_graph (bool, optional) – If False, the graph used to compute the grads will be freed. Note that in nearly all cases setting this option to True is not needed and often can be worked around in a much more efficient way. Defaults to the value of create_graph. 
create_graph (bool, optional) – If True, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to False. 
inputs (sequence of Tensor) – Inputs w.r.t. which the gradient will be accumulated into .grad. All other Tensors will be ignored. If not provided, the gradient is accumulated into all the leaf Tensors that were used to compute the attr::tensors. All the provided inputs must be leaf Tensors.    
  
baddbmm(batch1, batch2, *, beta=1, alpha=1) → Tensor  
See torch.baddbmm() 
  
baddbmm_(batch1, batch2, *, beta=1, alpha=1) → Tensor  
In-place version of baddbmm() 
  
bernoulli(*, generator=None) → Tensor  
Returns a result tensor where each result[i]\texttt{result[i]}  is independently sampled from Bernoulli(self[i])\text{Bernoulli}(\texttt{self[i]}) . self must have floating point dtype, and the result will have the same dtype. See torch.bernoulli() 
  
bernoulli_()  
 
bernoulli_(p=0.5, *, generator=None) → Tensor  
Fills each location of self with an independent sample from Bernoulli(p)\text{Bernoulli}(\texttt{p}) . self can have integral dtype. 
  
bernoulli_(p_tensor, *, generator=None) → Tensor  
p_tensor should be a tensor containing probabilities to be used for drawing the binary random number. The ith\text{i}^{th}  element of self tensor will be set to a value sampled from Bernoulli(p_tensor[i])\text{Bernoulli}(\texttt{p\_tensor[i]}) . self can have integral dtype, but p_tensor must have floating point dtype. 
 See also bernoulli() and torch.bernoulli() 
  
bfloat16(memory_format=torch.preserve_format) → Tensor  
self.bfloat16() is equivalent to self.to(torch.bfloat16). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
bincount(weights=None, minlength=0) → Tensor  
See torch.bincount() 
  
bitwise_not() → Tensor  
See torch.bitwise_not() 
  
bitwise_not_() → Tensor  
In-place version of bitwise_not() 
  
bitwise_and() → Tensor  
See torch.bitwise_and() 
  
bitwise_and_() → Tensor  
In-place version of bitwise_and() 
  
bitwise_or() → Tensor  
See torch.bitwise_or() 
  
bitwise_or_() → Tensor  
In-place version of bitwise_or() 
  
bitwise_xor() → Tensor  
See torch.bitwise_xor() 
  
bitwise_xor_() → Tensor  
In-place version of bitwise_xor() 
  
bmm(batch2) → Tensor  
See torch.bmm() 
  
bool(memory_format=torch.preserve_format) → Tensor  
self.bool() is equivalent to self.to(torch.bool). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
byte(memory_format=torch.preserve_format) → Tensor  
self.byte() is equivalent to self.to(torch.uint8). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
broadcast_to(shape) → Tensor  
See torch.broadcast_to(). 
  
cauchy_(median=0, sigma=1, *, generator=None) → Tensor  
Fills the tensor with numbers drawn from the Cauchy distribution:  f(x)=1πσ(x−median)2+σ2f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2} 

  
ceil() → Tensor  
See torch.ceil() 
  
ceil_() → Tensor  
In-place version of ceil() 
  
char(memory_format=torch.preserve_format) → Tensor  
self.char() is equivalent to self.to(torch.int8). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
cholesky(upper=False) → Tensor  
See torch.cholesky() 
  
cholesky_inverse(upper=False) → Tensor  
See torch.cholesky_inverse() 
  
cholesky_solve(input2, upper=False) → Tensor  
See torch.cholesky_solve() 
  
chunk(chunks, dim=0) → List of Tensors  
See torch.chunk() 
  
clamp(min, max) → Tensor  
See torch.clamp() 
  
clamp_(min, max) → Tensor  
In-place version of clamp() 
  
clip(min, max) → Tensor  
Alias for clamp(). 
  
clip_(min, max) → Tensor  
Alias for clamp_(). 
  
clone(*, memory_format=torch.preserve_format) → Tensor  
See torch.clone() 
  
contiguous(memory_format=torch.contiguous_format) → Tensor  
Returns a contiguous in memory tensor containing the same data as self tensor. If self tensor is already in the specified memory format, this function returns the self tensor.  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.contiguous_format.   
  
copy_(src, non_blocking=False) → Tensor  
Copies the elements from src into self tensor and returns self. The src tensor must be broadcastable with the self tensor. It may be of a different data type or reside on a different device.  Parameters 
 
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.    
  
conj() → Tensor  
See torch.conj() 
  
copysign(other) → Tensor  
See torch.copysign() 
  
copysign_(other) → Tensor  
In-place version of copysign() 
  
cos() → Tensor  
See torch.cos() 
  
cos_() → Tensor  
In-place version of cos() 
  
cosh() → Tensor  
See torch.cosh() 
  
cosh_() → Tensor  
In-place version of cosh() 
  
count_nonzero(dim=None) → Tensor  
See torch.count_nonzero() 
  
acosh() → Tensor  
See torch.acosh() 
  
acosh_() → Tensor  
In-place version of acosh() 
  
arccosh()  
acosh() -> Tensor See torch.arccosh() 
  
arccosh_()  
acosh_() -> Tensor In-place version of arccosh() 
  
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.  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
cross(other, dim=-1) → Tensor  
See torch.cross() 
  
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.  Parameters 
 
device (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 (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.    
  
logcumsumexp(dim) → Tensor  
See torch.logcumsumexp() 
  
cummax(dim) -> (Tensor, Tensor)  
See torch.cummax() 
  
cummin(dim) -> (Tensor, Tensor)  
See torch.cummin() 
  
cumprod(dim, dtype=None) → Tensor  
See torch.cumprod() 
  
cumprod_(dim, dtype=None) → Tensor  
In-place version of cumprod() 
  
cumsum(dim, dtype=None) → Tensor  
See torch.cumsum() 
  
cumsum_(dim, dtype=None) → Tensor  
In-place version of cumsum() 
  
data_ptr() → int  
Returns the address of the first element of self tensor. 
  
deg2rad() → Tensor  
See torch.deg2rad() 
  
dequantize() → Tensor  
Given a quantized Tensor, dequantize it and return the dequantized float Tensor. 
  
det() → Tensor  
See torch.det() 
  
dense_dim() → int  
Return the number of dense dimensions in a sparse tensor self.  Warning Throws an error if self is not a sparse tensor.  See also Tensor.sparse_dim() and hybrid tensors. 
  
detach()  
Returns a new Tensor, detached from the current graph. The result will never require gradient.  Note Returned Tensor shares the same storage with the original one. In-place modifications on either of them will be seen, and may trigger errors in correctness checks. IMPORTANT NOTE: Previously, in-place size / stride / storage changes (such as resize_ / resize_as_ / set_ / transpose_) to the returned tensor also update the original tensor. Now, these in-place changes will not update the original tensor anymore, and will instead trigger an error. For sparse tensors: In-place indices / values changes (such as zero_ / copy_ / add_) to the returned tensor will not update the original tensor anymore, and will instead trigger an error.  
  
detach_()  
Detaches the Tensor from the graph that created it, making it a leaf. Views cannot be detached in-place. 
  
diag(diagonal=0) → Tensor  
See torch.diag() 
  
diag_embed(offset=0, dim1=-2, dim2=-1) → Tensor  
See torch.diag_embed() 
  
diagflat(offset=0) → Tensor  
See torch.diagflat() 
  
diagonal(offset=0, dim1=0, dim2=1) → Tensor  
See torch.diagonal() 
  
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.  Parameters 
 
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.]])
 
  
fmax(other) → Tensor  
See torch.fmax() 
  
fmin(other) → Tensor  
See torch.fmin() 
  
diff(n=1, dim=-1, prepend=None, append=None) → Tensor  
See torch.diff() 
  
digamma() → Tensor  
See torch.digamma() 
  
digamma_() → Tensor  
In-place version of digamma() 
  
dim() → int  
Returns the number of dimensions of self tensor. 
  
dist(other, p=2) → Tensor  
See torch.dist() 
  
div(value, *, rounding_mode=None) → Tensor  
See torch.div() 
  
div_(value, *, rounding_mode=None) → Tensor  
In-place version of div() 
  
divide(value, *, rounding_mode=None) → Tensor  
See torch.divide() 
  
divide_(value, *, rounding_mode=None) → Tensor  
In-place version of divide() 
  
dot(other) → Tensor  
See torch.dot() 
  
double(memory_format=torch.preserve_format) → Tensor  
self.double() is equivalent to self.to(torch.float64). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
eig(eigenvectors=False) -> (Tensor, Tensor)  
See torch.eig() 
  
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
 
  
eq(other) → Tensor  
See torch.eq() 
  
eq_(other) → Tensor  
In-place version of eq() 
  
equal(other) → bool  
See torch.equal() 
  
erf() → Tensor  
See torch.erf() 
  
erf_() → Tensor  
In-place version of erf() 
  
erfc() → Tensor  
See torch.erfc() 
  
erfc_() → Tensor  
In-place version of erfc() 
  
erfinv() → Tensor  
See torch.erfinv() 
  
erfinv_() → Tensor  
In-place version of erfinv() 
  
exp() → Tensor  
See torch.exp() 
  
exp_() → Tensor  
In-place version of exp() 
  
expm1() → Tensor  
See torch.expm1() 
  
expm1_() → Tensor  
In-place version of expm1() 
  
expand(*sizes) → Tensor  
Returns a new view of the 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.  Parameters 
*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]])
 
  
expand_as(other) → Tensor  
Expand this tensor to the same size as other. self.expand_as(other) is equivalent to self.expand(other.size()). Please see expand() for more information about expand.  Parameters 
other (torch.Tensor) – The result tensor has the same size as other.   
  
exponential_(lambd=1, *, generator=None) → Tensor  
Fills self tensor with elements drawn from the exponential distribution:  f(x)=λe−λxf(x) = \lambda e^{-\lambda x} 

  
fix() → Tensor  
See torch.fix(). 
  
fix_() → Tensor  
In-place version of fix() 
  
fill_(value) → Tensor  
Fills self tensor with the specified value. 
  
flatten(input, start_dim=0, end_dim=-1) → Tensor  
see torch.flatten() 
  
flip(dims) → Tensor  
See torch.flip() 
  
fliplr() → Tensor  
See torch.fliplr() 
  
flipud() → Tensor  
See torch.flipud() 
  
float(memory_format=torch.preserve_format) → Tensor  
self.float() is equivalent to self.to(torch.float32). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
float_power(exponent) → Tensor  
See torch.float_power() 
  
float_power_(exponent) → Tensor  
In-place version of float_power() 
  
floor() → Tensor  
See torch.floor() 
  
floor_() → Tensor  
In-place version of floor() 
  
floor_divide(value) → Tensor  
See torch.floor_divide() 
  
floor_divide_(value) → Tensor  
In-place version of floor_divide() 
  
fmod(divisor) → Tensor  
See torch.fmod() 
  
fmod_(divisor) → Tensor  
In-place version of fmod() 
  
frac() → Tensor  
See torch.frac() 
  
frac_() → Tensor  
In-place version of frac() 
  
gather(dim, index) → Tensor  
See torch.gather() 
  
gcd(other) → Tensor  
See torch.gcd() 
  
gcd_(other) → Tensor  
In-place version of gcd() 
  
ge(other) → Tensor  
See torch.ge(). 
  
ge_(other) → Tensor  
In-place version of ge(). 
  
greater_equal(other) → Tensor  
See torch.greater_equal(). 
  
greater_equal_(other) → Tensor  
In-place version of greater_equal(). 
  
geometric_(p, *, generator=None) → Tensor  
Fills self tensor with elements drawn from the geometric distribution:  f(X=k)=pk−1(1−p)f(X=k) = p^{k - 1} (1 - p) 

  
geqrf() -> (Tensor, Tensor)  
See torch.geqrf() 
  
ger(vec2) → Tensor  
See torch.ger() 
  
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
 
  
gt(other) → Tensor  
See torch.gt(). 
  
gt_(other) → Tensor  
In-place version of gt(). 
  
greater(other) → Tensor  
See torch.greater(). 
  
greater_(other) → Tensor  
In-place version of greater(). 
  
half(memory_format=torch.preserve_format) → Tensor  
self.half() is equivalent to self.to(torch.float16). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
hardshrink(lambd=0.5) → Tensor  
See torch.nn.functional.hardshrink() 
  
heaviside(values) → Tensor  
See torch.heaviside() 
  
histc(bins=100, min=0, max=0) → Tensor  
See torch.histc() 
  
hypot(other) → Tensor  
See torch.hypot() 
  
hypot_(other) → Tensor  
In-place version of hypot() 
  
i0() → Tensor  
See torch.i0() 
  
i0_() → Tensor  
In-place version of i0() 
  
igamma(other) → Tensor  
See torch.igamma() 
  
igamma_(other) → Tensor  
In-place version of igamma() 
  
igammac(other) → Tensor  
See torch.igammac() 
  
igammac_(other) → Tensor  
In-place version of igammac() 
  
index_add_(dim, index, tensor) → Tensor  
Accumulate the elements of tensor into the self tensor by adding to the indices in the order given in index. For example, if dim == 0 and index[i] == j, then the ith row of tensor is added to the jth row of self. The dimth dimension of tensor must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised.  Note This operation may behave nondeterministically when given tensors on a CUDA device. See Reproducibility for more information.   Parameters 
 
dim (int) – dimension along which to index 
index (IntTensor or LongTensor) – indices of 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.]])
 
  
index_add(tensor1, dim, index, tensor2) → Tensor  
Out-of-place version of torch.Tensor.index_add_(). tensor1 corresponds to self in torch.Tensor.index_add_(). 
  
index_copy_(dim, index, tensor) → Tensor  
Copies the elements of tensor into the self tensor by selecting the indices in the order given in index. For example, if dim == 0 and index[i] == j, then the ith row of tensor is copied to the jth row of self. The dimth dimension of tensor must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised.  Note If index contains duplicate entries, multiple elements from tensor will be copied to the same index of self. The result is nondeterministic since it depends on which copy occurs last.   Parameters 
 
dim (int) – dimension along which to index 
index (LongTensor) – indices of 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.]])
 
  
index_copy(tensor1, dim, index, tensor2) → Tensor  
Out-of-place version of torch.Tensor.index_copy_(). tensor1 corresponds to self in torch.Tensor.index_copy_(). 
  
index_fill_(dim, index, val) → Tensor  
Fills the elements of the self tensor with value val by selecting the indices in the order given in index.  Parameters 
 
dim (int) – dimension along which to index 
index (LongTensor) – indices of 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.]])
   
  
index_fill(tensor1, dim, index, value) → Tensor  
Out-of-place version of torch.Tensor.index_fill_(). tensor1 corresponds to self in torch.Tensor.index_fill_(). 
  
index_put_(indices, values, accumulate=False) → Tensor  
Puts values from the tensor values into the tensor self using the indices specified in indices (which is a tuple of Tensors). The expression tensor.index_put_(indices, values) is equivalent to tensor[indices] = values. Returns self. If accumulate is True, the elements in values are added to self. If accumulate is False, the behavior is undefined if indices contain duplicate elements.  Parameters 
 
indices (tuple of LongTensor) – tensors used to index into self. 
values (Tensor) – tensor of same dtype as self. 
accumulate (bool) – whether to accumulate into self    
  
index_put(tensor1, indices, values, accumulate=False) → Tensor  
Out-place version of index_put_(). tensor1 corresponds to self in torch.Tensor.index_put_(). 
  
index_select(dim, index) → Tensor  
See torch.index_select() 
  
indices() → Tensor  
Return the indices tensor of a sparse COO tensor.  Warning Throws an error if self is not a sparse COO tensor.  See also Tensor.values().  Note This method can only be called on a coalesced sparse tensor. See Tensor.coalesce() for details.  
  
inner(other) → Tensor  
See torch.inner(). 
  
int(memory_format=torch.preserve_format) → Tensor  
self.int() is equivalent to self.to(torch.int32). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
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. 
  
inverse() → Tensor  
See torch.inverse() 
  
isclose(other, rtol=1e-05, atol=1e-08, equal_nan=False) → Tensor  
See torch.isclose() 
  
isfinite() → Tensor  
See torch.isfinite() 
  
isinf() → Tensor  
See torch.isinf() 
  
isposinf() → Tensor  
See torch.isposinf() 
  
isneginf() → Tensor  
See torch.isneginf() 
  
isnan() → Tensor  
See torch.isnan() 
  
is_contiguous(memory_format=torch.contiguous_format) → bool  
Returns True if self tensor is contiguous in memory in the order specified by memory format.  Parameters 
memory_format (torch.memory_format, optional) – Specifies memory allocation order. Default: torch.contiguous_format.   
  
is_complex() → bool  
Returns True if the data type of self is a complex data type. 
  
is_floating_point() → bool  
Returns True if the data type of self is a floating point data type. 
  
is_leaf  
All Tensors that have requires_grad which is False will be leaf Tensors by convention. For Tensors that have 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 grad_fn is None. Only leaf Tensors will have their grad populated during a call to backward(). To get grad populated for non-leaf Tensors, you can use 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
 
  
is_pinned()  
Returns true if this tensor resides in pinned memory. 
  
is_set_to(tensor) → bool  
Returns True if both tensors are pointing to the exact same memory (same storage, offset, size and stride). 
  
is_shared() [source]
 
Checks if tensor is in shared memory. This is always True for CUDA tensors. 
  
is_signed() → bool  
Returns True if the data type of self is a signed data type. 
  
is_sparse  
Is True if the Tensor uses sparse storage layout, False otherwise. 
  
istft(n_fft, hop_length=None, win_length=None, window=None, center=True, normalized=False, onesided=None, length=None, return_complex=False) [source]
 
See torch.istft() 
  
isreal() → Tensor  
See torch.isreal() 
  
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 tolist(). This operation is not differentiable. Example: >>> x = torch.tensor([1.0])
>>> x.item()
1.0
 
  
kthvalue(k, dim=None, keepdim=False) -> (Tensor, LongTensor)  
See torch.kthvalue() 
  
lcm(other) → Tensor  
See torch.lcm() 
  
lcm_(other) → Tensor  
In-place version of lcm() 
  
ldexp(other) → Tensor  
See torch.ldexp() 
  
ldexp_(other) → Tensor  
In-place version of ldexp() 
  
le(other) → Tensor  
See torch.le(). 
  
le_(other) → Tensor  
In-place version of le(). 
  
less_equal(other) → Tensor  
See torch.less_equal(). 
  
less_equal_(other) → Tensor  
In-place version of less_equal(). 
  
lerp(end, weight) → Tensor  
See torch.lerp() 
  
lerp_(end, weight) → Tensor  
In-place version of lerp() 
  
lgamma() → Tensor  
See torch.lgamma() 
  
lgamma_() → Tensor  
In-place version of lgamma() 
  
log() → Tensor  
See torch.log() 
  
log_() → Tensor  
In-place version of log() 
  
logdet() → Tensor  
See torch.logdet() 
  
log10() → Tensor  
See torch.log10() 
  
log10_() → Tensor  
In-place version of log10() 
  
log1p() → Tensor  
See torch.log1p() 
  
log1p_() → Tensor  
In-place version of log1p() 
  
log2() → Tensor  
See torch.log2() 
  
log2_() → Tensor  
In-place version of log2() 
  
log_normal_(mean=1, std=2, *, generator=None)  
Fills self tensor with numbers samples from the log-normal distribution parameterized by the given mean μ\mu  and standard deviation σ\sigma . Note that mean and std are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution:  f(x)=1xσ2πe−(ln⁡x−μ)22σ2f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}} 

  
logaddexp(other) → Tensor  
See torch.logaddexp() 
  
logaddexp2(other) → Tensor  
See torch.logaddexp2() 
  
logsumexp(dim, keepdim=False) → Tensor  
See torch.logsumexp() 
  
logical_and() → Tensor  
See torch.logical_and() 
  
logical_and_() → Tensor  
In-place version of logical_and() 
  
logical_not() → Tensor  
See torch.logical_not() 
  
logical_not_() → Tensor  
In-place version of logical_not() 
  
logical_or() → Tensor  
See torch.logical_or() 
  
logical_or_() → Tensor  
In-place version of logical_or() 
  
logical_xor() → Tensor  
See torch.logical_xor() 
  
logical_xor_() → Tensor  
In-place version of logical_xor() 
  
logit() → Tensor  
See torch.logit() 
  
logit_() → Tensor  
In-place version of logit() 
  
long(memory_format=torch.preserve_format) → Tensor  
self.long() is equivalent to self.to(torch.int64). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
lstsq(A) -> (Tensor, Tensor)  
See torch.lstsq() 
  
lt(other) → Tensor  
See torch.lt(). 
  
lt_(other) → Tensor  
In-place version of lt(). 
  
less()  
lt(other) -> Tensor See torch.less(). 
  
less_(other) → Tensor  
In-place version of less(). 
  
lu(pivot=True, get_infos=False) [source]
 
See torch.lu() 
  
lu_solve(LU_data, LU_pivots) → Tensor  
See torch.lu_solve() 
  
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. 
  
map_(tensor, callable)  
Applies callable for each element in self tensor and the given tensor and stores the results in self tensor. self tensor and the given tensor must be broadcastable. The callable should have the signature: def callable(a, b) -> number
 
  
masked_scatter_(mask, source)  
Copies elements from source into self tensor at positions where the mask is True. The shape of mask must be broadcastable with the shape of the underlying tensor. The source should have at least as many elements as the number of ones in mask  Parameters 
 
mask (BoolTensor) – the boolean mask 
source (Tensor) – the tensor to copy from     Note The mask operates on the self tensor, not on the given source tensor.  
  
masked_scatter(mask, tensor) → Tensor  
Out-of-place version of torch.Tensor.masked_scatter_() 
  
masked_fill_(mask, value)  
Fills elements of self tensor with value where mask is True. The shape of mask must be broadcastable with the shape of the underlying tensor.  Parameters 
 
mask (BoolTensor) – the boolean mask 
value (float) – the value to fill in with    
  
masked_fill(mask, value) → Tensor  
Out-of-place version of torch.Tensor.masked_fill_() 
  
masked_select(mask) → Tensor  
See torch.masked_select() 
  
matmul(tensor2) → Tensor  
See torch.matmul() 
  
matrix_power(n) → Tensor  
See torch.matrix_power() 
  
matrix_exp() → Tensor  
See torch.matrix_exp() 
  
max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)  
See torch.max() 
  
maximum(other) → Tensor  
See torch.maximum() 
  
mean(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)  
See torch.mean() 
  
median(dim=None, keepdim=False) -> (Tensor, LongTensor)  
See torch.median() 
  
nanmedian(dim=None, keepdim=False) -> (Tensor, LongTensor)  
See torch.nanmedian() 
  
min(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)  
See torch.min() 
  
minimum(other) → Tensor  
See torch.minimum() 
  
mm(mat2) → Tensor  
See torch.mm() 
  
smm(mat) → Tensor  
See torch.smm() 
  
mode(dim=None, keepdim=False) -> (Tensor, LongTensor)  
See torch.mode() 
  
movedim(source, destination) → Tensor  
See torch.movedim() 
  
moveaxis(source, destination) → Tensor  
See torch.moveaxis() 
  
msort() → Tensor  
See torch.msort() 
  
mul(value) → Tensor  
See torch.mul(). 
  
mul_(value) → Tensor  
In-place version of mul(). 
  
multiply(value) → Tensor  
See torch.multiply(). 
  
multiply_(value) → Tensor  
In-place version of multiply(). 
  
multinomial(num_samples, replacement=False, *, generator=None) → Tensor  
See torch.multinomial() 
  
mv(vec) → Tensor  
See torch.mv() 
  
mvlgamma(p) → Tensor  
See torch.mvlgamma() 
  
mvlgamma_(p) → Tensor  
In-place version of mvlgamma() 
  
nansum(dim=None, keepdim=False, dtype=None) → Tensor  
See torch.nansum() 
  
narrow(dimension, start, length) → Tensor  
See 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]])
 
  
narrow_copy(dimension, start, length) → Tensor  
Same as 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. 
  
ndimension() → int  
Alias for dim() 
  
nan_to_num(nan=0.0, posinf=None, neginf=None) → Tensor  
See torch.nan_to_num(). 
  
nan_to_num_(nan=0.0, posinf=None, neginf=None) → Tensor  
In-place version of nan_to_num(). 
  
ne(other) → Tensor  
See torch.ne(). 
  
ne_(other) → Tensor  
In-place version of ne(). 
  
not_equal(other) → Tensor  
See torch.not_equal(). 
  
not_equal_(other) → Tensor  
In-place version of not_equal(). 
  
neg() → Tensor  
See torch.neg() 
  
neg_() → Tensor  
In-place version of neg() 
  
negative() → Tensor  
See torch.negative() 
  
negative_() → Tensor  
In-place version of negative() 
  
nelement() → int  
Alias for numel() 
  
nextafter(other) → Tensor  
See torch.nextafter() 
  
nextafter_(other) → Tensor  
In-place version of nextafter() 
  
nonzero() → LongTensor  
See torch.nonzero() 
  
norm(p='fro', dim=None, keepdim=False, dtype=None) [source]
 
See torch.norm() 
  
normal_(mean=0, std=1, *, generator=None) → Tensor  
Fills self tensor with elements samples from the normal distribution parameterized by mean and std. 
  
numel() → int  
See torch.numel() 
  
numpy() → numpy.ndarray  
Returns self tensor as a NumPy ndarray. This tensor and the returned ndarray share the same underlying storage. Changes to self tensor will be reflected in the ndarray and vice versa. 
  
orgqr(input2) → Tensor  
See torch.orgqr() 
  
ormqr(input2, input3, left=True, transpose=False) → Tensor  
See torch.ormqr() 
  
outer(vec2) → Tensor  
See torch.outer(). 
  
permute(*dims) → Tensor  
Returns a view of the original tensor with its dimensions permuted.  Parameters 
*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])
 
  
pin_memory() → Tensor  
Copies the tensor to pinned memory, if it’s not already pinned. 
  
pinverse() → Tensor  
See torch.pinverse() 
  
polygamma(n) → Tensor  
See torch.polygamma() 
  
polygamma_(n) → Tensor  
In-place version of polygamma() 
  
pow(exponent) → Tensor  
See torch.pow() 
  
pow_(exponent) → Tensor  
In-place version of pow() 
  
prod(dim=None, keepdim=False, dtype=None) → Tensor  
See torch.prod() 
  
put_(indices, tensor, accumulate=False) → Tensor  
Copies the elements from tensor into the positions specified by indices. For the purpose of indexing, the self tensor is treated as if it were a 1-D tensor. If accumulate is True, the elements in tensor are added to self. If accumulate is False, the behavior is undefined if indices contain duplicate elements.  Parameters 
 
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]])
 
  
qr(some=True) -> (Tensor, Tensor)  
See torch.qr() 
  
qscheme() → torch.qscheme  
Returns the quantization scheme of a given QTensor. 
  
quantile(q, dim=None, keepdim=False) → Tensor  
See torch.quantile() 
  
nanquantile(q, dim=None, keepdim=False) → Tensor  
See torch.nanquantile() 
  
q_scale() → float  
Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer(). 
  
q_zero_point() → int  
Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer(). 
  
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. 
  
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. 
  
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. 
  
rad2deg() → Tensor  
See torch.rad2deg() 
  
random_(from=0, to=None, *, generator=None) → Tensor  
Fills self tensor with numbers sampled from the discrete uniform distribution over [from, to - 1]. If not specified, the values are usually only bounded by 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]. 
  
ravel(input) → Tensor  
see torch.ravel() 
  
reciprocal() → Tensor  
See torch.reciprocal() 
  
reciprocal_() → Tensor  
In-place version of reciprocal() 
  
record_stream(stream)  
Ensures that the tensor memory is not reused for another tensor until all current work queued on 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.  
  
register_hook(hook) [source]
 
Registers a backward hook. The hook will be called every time a gradient with respect to the Tensor is computed. The hook should have the following signature: hook(grad) -> Tensor or None
 The hook should not modify its argument, but it can optionally return a new gradient which will be used in place of grad. This function returns a handle with a method handle.remove() that removes the hook from the module. Example: >>> v = torch.tensor([0., 0., 0.], requires_grad=True)
>>> h = v.register_hook(lambda grad: grad * 2)  # double the gradient
>>> v.backward(torch.tensor([1., 2., 3.]))
>>> v.grad

 2
 4
 6
[torch.FloatTensor of size (3,)]

>>> h.remove()  # removes the hook
 
  
remainder(divisor) → Tensor  
See torch.remainder() 
  
remainder_(divisor) → Tensor  
In-place version of remainder() 
  
renorm(p, dim, maxnorm) → Tensor  
See torch.renorm() 
  
renorm_(p, dim, maxnorm) → Tensor  
In-place version of renorm() 
  
repeat(*sizes) → Tensor  
Repeats this tensor along the specified dimensions. Unlike expand(), this function copies the tensor’s data.  Warning repeat() behaves differently from numpy.repeat, but is more similar to numpy.tile. For the operator similar to numpy.repeat, see torch.repeat_interleave().   Parameters 
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])
 
  
repeat_interleave(repeats, dim=None) → Tensor  
See torch.repeat_interleave(). 
  
requires_grad  
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 grad attribute will be populated, see is_leaf for more details.  
  
requires_grad_(requires_grad=True) → Tensor  
Change if autograd should record operations on this tensor: sets this tensor’s requires_grad attribute in-place. Returns this tensor. 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.  Parameters 
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])
 
  
reshape(*shape) → Tensor  
Returns a tensor with the same data and number of elements as self but with the specified shape. This method returns a view if shape is compatible with the current shape. See torch.Tensor.view() on when it is possible to return a view. See torch.reshape()  Parameters 
shape (tuple of python:ints or int...) – the desired shape   
  
reshape_as(other) → Tensor  
Returns this tensor as the same shape as 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 torch.Tensor.view() on when it is possible to return a view. Please see reshape() for more information about reshape.  Parameters 
other (torch.Tensor) – The result tensor has the same shape as other.   
  
resize_(*sizes, memory_format=torch.contiguous_format) → Tensor  
Resizes 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 view(), which checks for contiguity, or reshape(), which copies data if needed. To change the size in-place with custom strides, see set_().   Parameters 
 
sizes (torch.Size or int...) – the desired size 
memory_format (torch.memory_format, optional) – the desired memory format of Tensor. Default: torch.contiguous_format. Note that memory format of 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]])
 
  
resize_as_(tensor, memory_format=torch.contiguous_format) → Tensor  
Resizes the self tensor to be the same size as the specified tensor. This is equivalent to self.resize_(tensor.size()).  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of Tensor. Default: torch.contiguous_format. Note that memory format of self is going to be unaffected if self.size() matches tensor.size().   
  
retain_grad() [source]
 
Enables .grad attribute for non-leaf Tensors. 
  
roll(shifts, dims) → Tensor  
See torch.roll() 
  
rot90(k, dims) → Tensor  
See torch.rot90() 
  
round() → Tensor  
See torch.round() 
  
round_() → Tensor  
In-place version of round() 
  
rsqrt() → Tensor  
See torch.rsqrt() 
  
rsqrt_() → Tensor  
In-place version of rsqrt() 
  
scatter(dim, index, src) → Tensor  
Out-of-place version of torch.Tensor.scatter_() 
  
scatter_(dim, index, src, reduce=None) → Tensor  
Writes all values from the tensor src into self at the indices specified in the index tensor. For each value in src, its output index is specified by its index in src for dimension != dim and by the corresponding value in index for dimension = dim. For a 3-D tensor, 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 gather(). self, index and src (if it is a Tensor) should all have the 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 that index and src do not broadcast. Moreover, as for gather(), the values of index must be between 0 and self.size(dim) - 1 inclusive.  Warning When indices are not unique, the behavior is non-deterministic (one of the values from src will be picked arbitrarily) and the gradient will be incorrect (it will be propagated to all locations in the source that correspond to the same index)!   Note The backward pass is implemented only for src.shape == index.shape.  Additionally accepts an optional reduce argument that allows specification of an optional reduction operation, which is applied to all values in the tensor src into self at the indicies specified in the index. For each value in src, the reduction operation is applied to an index in self which is specified by its index in src for dimension != dim and by the corresponding value in index for dimension = dim. Given a 3-D tensor and reduction using the multiplication operation, 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 scatter_add_().  Parameters 
 
dim (int) – the axis along which to index 
index (LongTensor) – the indices of elements to scatter, can be either empty or of the same dimensionality as src. When empty, the operation returns self unchanged. 
src (Tensor or float) – the source element(s) to scatter. 
reduce (str, optional) – reduction operation to apply, can be either 'add' or 'multiply'.    Example: >>> src = torch.arange(1, 11).reshape((2, 5))
>>> src
tensor([[ 1,  2,  3,  4,  5],
        [ 6,  7,  8,  9, 10]])
>>> index = torch.tensor([[0, 1, 2, 0]])
>>> torch.zeros(3, 5, dtype=src.dtype).scatter_(0, index, src)
tensor([[1, 0, 0, 4, 0],
        [0, 2, 0, 0, 0],
        [0, 0, 3, 0, 0]])
>>> index = torch.tensor([[0, 1, 2], [0, 1, 4]])
>>> torch.zeros(3, 5, dtype=src.dtype).scatter_(1, index, src)
tensor([[1, 2, 3, 0, 0],
        [6, 7, 0, 0, 8],
        [0, 0, 0, 0, 0]])

>>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]),
...            1.23, reduce='multiply')
tensor([[2.0000, 2.0000, 2.4600, 2.0000],
        [2.0000, 2.0000, 2.0000, 2.4600]])
>>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]),
...            1.23, reduce='add')
tensor([[2.0000, 2.0000, 3.2300, 2.0000],
        [2.0000, 2.0000, 2.0000, 3.2300]])
 
  
scatter_add_(dim, index, src) → Tensor  
Adds all values from the tensor other into self at the indices specified in the index tensor in a similar fashion as scatter_(). For each value in src, it is added to an index in self which is specified by its index in src for dimension != dim and by the corresponding value in index for dimension = dim. For a 3-D tensor, 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
 self, index and 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 that index and src do not broadcast.  Note This operation may behave nondeterministically when given tensors on a CUDA device. See Reproducibility for more information.   Note The backward pass is implemented only for src.shape == index.shape.   Parameters 
 
dim (int) – the axis along which to index 
index (LongTensor) – the indices of elements to scatter and add, can be either empty or of the same dimensionality as src. When empty, the operation returns self unchanged. 
src (Tensor) – the source elements to scatter and add    Example: >>> src = torch.ones((2, 5))
>>> index = torch.tensor([[0, 1, 2, 0, 0]])
>>> torch.zeros(3, 5, dtype=src.dtype).scatter_add_(0, index, src)
tensor([[1., 0., 0., 1., 1.],
        [0., 1., 0., 0., 0.],
        [0., 0., 1., 0., 0.]])
>>> index = torch.tensor([[0, 1, 2, 0, 0], [0, 1, 2, 2, 2]])
>>> torch.zeros(3, 5, dtype=src.dtype).scatter_add_(0, index, src)
tensor([[2., 0., 0., 1., 1.],
        [0., 2., 0., 0., 0.],
        [0., 0., 2., 1., 1.]])
 
  
scatter_add(dim, index, src) → Tensor  
Out-of-place version of torch.Tensor.scatter_add_() 
  
select(dim, index) → Tensor  
Slices the self tensor along the selected dimension at the given index. This function returns a view of the original tensor with the given dimension removed.  Parameters 
 
dim (int) – the dimension to slice 
index (int) – the index to select with     Note 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].  
  
set_(source=None, storage_offset=0, size=None, stride=None) → Tensor  
Sets the underlying storage, size, and strides. If source is a tensor, self tensor will share the same storage and have the same size and strides as source. Changes to elements in one tensor will be reflected in the other. If source is a Storage, the method sets the underlying storage, offset, size, and stride.  Parameters 
 
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.    
  
share_memory_() [source]
 
Moves the underlying storage to shared memory. This is a no-op if the underlying storage is already in shared memory and for CUDA tensors. Tensors in shared memory cannot be resized. 
  
short(memory_format=torch.preserve_format) → Tensor  
self.short() is equivalent to self.to(torch.int16). See to().  Parameters 
memory_format (torch.memory_format, optional) – the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
sigmoid() → Tensor  
See torch.sigmoid() 
  
sigmoid_() → Tensor  
In-place version of sigmoid() 
  
sign() → Tensor  
See torch.sign() 
  
sign_() → Tensor  
In-place version of sign() 
  
signbit() → Tensor  
See torch.signbit() 
  
sgn() → Tensor  
See torch.sgn() 
  
sgn_() → Tensor  
In-place version of sgn() 
  
sin() → Tensor  
See torch.sin() 
  
sin_() → Tensor  
In-place version of sin() 
  
sinc() → Tensor  
See torch.sinc() 
  
sinc_() → Tensor  
In-place version of sinc() 
  
sinh() → Tensor  
See torch.sinh() 
  
sinh_() → Tensor  
In-place version of sinh() 
  
asinh() → Tensor  
See torch.asinh() 
  
asinh_() → Tensor  
In-place version of asinh() 
  
arcsinh() → Tensor  
See torch.arcsinh() 
  
arcsinh_() → Tensor  
In-place version of arcsinh() 
  
size() → torch.Size  
Returns the size of the self tensor. The returned value is a subclass of tuple. Example: >>> torch.empty(3, 4, 5).size()
torch.Size([3, 4, 5])
 
  
slogdet() -> (Tensor, Tensor)  
See torch.slogdet() 
  
solve(A) → Tensor, Tensor  
See torch.solve() 
  
sort(dim=-1, descending=False) -> (Tensor, LongTensor)  
See torch.sort() 
  
split(split_size, dim=0) [source]
 
See torch.split() 
  
sparse_mask(mask) → Tensor  
Returns a new sparse tensor with values from a strided tensor self filtered by the indices of the sparse tensor mask. The values of mask sparse tensor are ignored. self and mask tensors must have the same shape.  Note The returned sparse tensor has the same indices as the sparse tensor mask, even when the corresponding values in self are zeros.   Parameters 
mask (Tensor) – a sparse tensor whose indices are used as a filter   Example: >>> nse = 5
>>> dims = (5, 5, 2, 2)
>>> I = torch.cat([torch.randint(0, dims[0], size=(nse,)),
...                torch.randint(0, dims[1], size=(nse,))], 0).reshape(2, nse)
>>> V = torch.randn(nse, dims[2], dims[3])
>>> S = torch.sparse_coo_tensor(I, V, dims).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)
 
  
sparse_dim() → int  
Return the number of sparse dimensions in a sparse tensor self.  Warning Throws an error if self is not a sparse tensor.  See also Tensor.dense_dim() and hybrid tensors. 
  
sqrt() → Tensor  
See torch.sqrt() 
  
sqrt_() → Tensor  
In-place version of sqrt() 
  
square() → Tensor  
See torch.square() 
  
square_() → Tensor  
In-place version of square() 
  
squeeze(dim=None) → Tensor  
See torch.squeeze() 
  
squeeze_(dim=None) → Tensor  
In-place version of squeeze() 
  
std(dim=None, unbiased=True, keepdim=False) → Tensor  
See torch.std() 
  
stft(n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode='reflect', normalized=False, onesided=None, return_complex=None) [source]
 
See torch.stft()  Warning This function changed signature at version 0.4.1. Calling with the previous signature may cause error or return incorrect result.  
  
storage() → torch.Storage  
Returns the underlying storage. 
  
storage_offset() → int  
Returns 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
 
  
storage_type() → type  
Returns the type of the underlying storage. 
  
stride(dim) → tuple or int  
Returns the stride of self tensor. Stride is the jump necessary to go from one element to the next one in the specified dimension 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 dim.  Parameters 
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
 
  
sub(other, *, alpha=1) → Tensor  
See torch.sub(). 
  
sub_(other, *, alpha=1) → Tensor  
In-place version of sub() 
  
subtract(other, *, alpha=1) → Tensor  
See torch.subtract(). 
  
subtract_(other, *, alpha=1) → Tensor  
In-place version of subtract(). 
  
sum(dim=None, keepdim=False, dtype=None) → Tensor  
See torch.sum() 
  
sum_to_size(*size) → Tensor  
Sum this tensor to size. size must be broadcastable to this tensor size.  Parameters 
size (int...) – a sequence of integers defining the shape of the output tensor.   
  
svd(some=True, compute_uv=True) -> (Tensor, Tensor, Tensor)  
See torch.svd() 
  
swapaxes(axis0, axis1) → Tensor  
See torch.swapaxes() 
  
swapdims(dim0, dim1) → Tensor  
See torch.swapdims() 
  
symeig(eigenvectors=False, upper=True) -> (Tensor, Tensor)  
See torch.symeig() 
  
t() → Tensor  
See torch.t() 
  
t_() → Tensor  
In-place version of t() 
  
tensor_split(indices_or_sections, dim=0) → List of Tensors  
See torch.tensor_split() 
  
tile(*reps) → Tensor  
See torch.tile() 
  
to(*args, **kwargs) → Tensor  
Performs Tensor dtype and/or device conversion. A torch.dtype and torch.device are inferred from the arguments of self.to(*args, **kwargs).  Note If the self Tensor already has the correct torch.dtype and torch.device, then self is returned. Otherwise, the returned tensor is a copy of self with the desired torch.dtype and torch.device.  Here are the ways to call to:  
to(dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format) → Tensor  
Returns a Tensor with the specified dtype  Args:

memory_format (torch.memory_format, optional): the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
to(device=None, dtype=None, non_blocking=False, copy=False, memory_format=torch.preserve_format) → Tensor  
Returns a Tensor with the specified device and (optional) dtype. If dtype is None it is inferred to be self.dtype. When 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 copy is set, a new Tensor is created even when the Tensor already matches the desired conversion.  Args:

memory_format (torch.memory_format, optional): the desired memory format of returned Tensor. Default: torch.preserve_format.   
  
to(other, non_blocking=False, copy=False) → Tensor  
Returns a Tensor with same torch.dtype and torch.device as the Tensor other. When 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 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')
 
  
to_mkldnn() → Tensor  
Returns a copy of the tensor in torch.mkldnn layout. 
  
take(indices) → Tensor  
See torch.take() 
  
tan() → Tensor  
See torch.tan() 
  
tan_() → Tensor  
In-place version of tan() 
  
tanh() → Tensor  
See torch.tanh() 
  
tanh_() → Tensor  
In-place version of tanh() 
  
atanh() → Tensor  
See torch.atanh() 
  
atanh_(other) → Tensor  
In-place version of atanh() 
  
arctanh() → Tensor  
See torch.arctanh() 
  
arctanh_(other) → Tensor  
In-place version of arctanh() 
  
tolist() → list or number  
Returns the tensor as a (nested) list. For scalars, a standard Python number is returned, just like with 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
 
  
topk(k, dim=None, largest=True, sorted=True) -> (Tensor, LongTensor)  
See torch.topk() 
  
to_sparse(sparseDims) → Tensor  
Returns a sparse copy of the tensor. PyTorch supports sparse tensors in coordinate format.  Parameters 
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)
 
  
trace() → Tensor  
See torch.trace() 
  
transpose(dim0, dim1) → Tensor  
See torch.transpose() 
  
transpose_(dim0, dim1) → Tensor  
In-place version of transpose() 
  
triangular_solve(A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor)  
See torch.triangular_solve() 
  
tril(k=0) → Tensor  
See torch.tril() 
  
tril_(k=0) → Tensor  
In-place version of tril() 
  
triu(k=0) → Tensor  
See torch.triu() 
  
triu_(k=0) → Tensor  
In-place version of triu() 
  
true_divide(value) → Tensor  
See torch.true_divide() 
  
true_divide_(value) → Tensor  
In-place version of true_divide_() 
  
trunc() → Tensor  
See torch.trunc() 
  
trunc_() → Tensor  
In-place version of trunc() 
  
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.  Parameters 
 
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.    
  
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())  Parameters 
tensor (Tensor) – the tensor which has the desired type   
  
unbind(dim=0) → seq  
See torch.unbind() 
  
unfold(dimension, size, step) → Tensor  
Returns a view of the original tensor which contains all slices of size size from self tensor in the dimension dimension. Step between two slices is given by step. If sizedim is the size of dimension dimension for self, the size of dimension dimension in the returned tensor will be (sizedim - size) / step + 1. An additional dimension of size size is appended in the returned tensor.  Parameters 
 
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.]])
 
  
uniform_(from=0, to=1) → Tensor  
Fills self tensor with numbers sampled from the continuous uniform distribution:  P(x)=1to−fromP(x) = \dfrac{1}{\text{to} - \text{from}}  

  
unique(sorted=True, return_inverse=False, return_counts=False, dim=None) [source]
 
Returns the unique elements of the input tensor. See torch.unique() 
  
unique_consecutive(return_inverse=False, return_counts=False, dim=None) [source]
 
Eliminates all but the first element from every consecutive group of equivalent elements. See torch.unique_consecutive() 
  
unsqueeze(dim) → Tensor  
See torch.unsqueeze() 
  
unsqueeze_(dim) → Tensor  
In-place version of unsqueeze() 
  
values() → Tensor  
Return the values tensor of a sparse COO tensor.  Warning Throws an error if self is not a sparse COO tensor.  See also Tensor.indices().  Note This method can only be called on a coalesced sparse tensor. See Tensor.coalesce() for details.  
  
var(dim=None, unbiased=True, keepdim=False) → Tensor  
See torch.var() 
  
vdot(other) → Tensor  
See torch.vdot() 
  
view(*shape) → Tensor  
Returns a new tensor with the same data as the self tensor but of a different 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 d,d+1,…,d+kd, d+1, \dots, d+k  that satisfy the following contiguity-like condition that ∀i=d,…,d+k−1\forall i = d, \dots, d+k-1 ,  stride[i]=stride[i+1]×size[i+1]\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1] 
Otherwise, it will not be possible to view self tensor as shape without copying it (e.g., via contiguous()). When it is unclear whether a view() can be performed, it is advisable to use reshape(), which returns a view if the shapes are compatible, and copies (equivalent to calling contiguous()) otherwise.  Parameters 
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
  
view(dtype) → Tensor 
 Returns a new tensor with the same data as the self tensor but of a different dtype. dtype must have the same number of bytes per element as self’s dtype.  Warning This overload is not supported by TorchScript, and using it in a Torchscript program will cause undefined behavior.   Parameters 
dtype (torch.dtype) – the desired dtype   Example: >>> x = torch.randn(4, 4)
>>> x
tensor([[ 0.9482, -0.0310,  1.4999, -0.5316],
        [-0.1520,  0.7472,  0.5617, -0.8649],
        [-2.4724, -0.0334, -0.2976, -0.8499],
        [-0.2109,  1.9913, -0.9607, -0.6123]])
>>> x.dtype
torch.float32

>>> y = x.view(torch.int32)
>>> y
tensor([[ 1064483442, -1124191867,  1069546515, -1089989247],
        [-1105482831,  1061112040,  1057999968, -1084397505],
        [-1071760287, -1123489973, -1097310419, -1084649136],
        [-1101533110,  1073668768, -1082790149, -1088634448]],
    dtype=torch.int32)
>>> y[0, 0] = 1000000000
>>> x
tensor([[ 0.0047, -0.0310,  1.4999, -0.5316],
        [-0.1520,  0.7472,  0.5617, -0.8649],
        [-2.4724, -0.0334, -0.2976, -0.8499],
        [-0.2109,  1.9913, -0.9607, -0.6123]])

>>> x.view(torch.int16)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: Viewing a tensor as a new dtype with a different number of bytes per element is not supported.
 
  
view_as(other) → Tensor  
View this tensor as the same size as other. self.view_as(other) is equivalent to self.view(other.size()). Please see view() for more information about view.  Parameters 
other (torch.Tensor) – The result tensor has the same size as other.   
  
where(condition, y) → Tensor  
self.where(condition, y) is equivalent to torch.where(condition, self, y). See torch.where() 
  
xlogy(other) → Tensor  
See torch.xlogy() 
  
xlogy_(other) → Tensor  
In-place version of xlogy() 
  
zero_() → Tensor  
Fills self tensor with zeros.