torch.Tensor

A torch.Tensor is a multi-dimensional matrix containing elements of a single data type.

Data types

Torch defines tensor types with the following data types:

Data type	dtype
32-bit floating point	torch.float32 or torch.float
64-bit floating point	torch.float64 or torch.double
16-bit floating point 1	torch.float16 or torch.half
16-bit floating point 2	torch.bfloat16
32-bit complex	torch.complex32 or torch.chalf
64-bit complex	torch.complex64 or torch.cfloat
128-bit complex	torch.complex128 or torch.cdouble
8-bit integer (unsigned)	torch.uint8
16-bit integer (unsigned)	torch.uint16 (limited support) 4
32-bit integer (unsigned)	torch.uint32 (limited support) 4
64-bit integer (unsigned)	torch.uint64 (limited support) 4
8-bit integer (signed)	torch.int8
16-bit integer (signed)	torch.int16 or torch.short
32-bit integer (signed)	torch.int32 or torch.int
64-bit integer (signed)	torch.int64 or torch.long
Boolean	torch.bool
quantized 8-bit integer (unsigned)	torch.quint8
quantized 8-bit integer (signed)	torch.qint8
quantized 32-bit integer (signed)	torch.qint32
quantized 4-bit integer (unsigned) 3	torch.quint4x2
8-bit floating point, e4m3 5	torch.float8_e4m3fn (limited support)
8-bit floating point, e5m2 5	torch.float8_e5m2 (limited support)

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

3

quantized 4-bit integer is stored as a 8-bit signed integer. Currently it’s only supported in EmbeddingBag operator.

4(1,2,3)

Unsigned types asides from uint8 are currently planned to only have limited support in eager mode (they primarily exist to assist usage with torch.compile); if you need eager support and the extra range is not needed, we recommend using their signed variants instead. See https://github.com/pytorch/pytorch/issues/58734 for more details.

5(1,2)

torch.float8_e4m3fn and torch.float8_e5m2 implement the spec for 8-bit floating point types from https://arxiv.org/abs/2209.05433. The op support is very limited.

For backwards compatibility, we support the following alternate class names for these data types:

Data type	CPU tensor	GPU tensor
32-bit floating point	torch.FloatTensor	torch.cuda.FloatTensor
64-bit floating point	torch.DoubleTensor	torch.cuda.DoubleTensor
16-bit floating point	torch.HalfTensor	torch.cuda.HalfTensor
16-bit floating point	torch.BFloat16Tensor	torch.cuda.BFloat16Tensor
8-bit integer (unsigned)	torch.ByteTensor	torch.cuda.ByteTensor
8-bit integer (signed)	torch.CharTensor	torch.cuda.CharTensor
16-bit integer (signed)	torch.ShortTensor	torch.cuda.ShortTensor
32-bit integer (signed)	torch.IntTensor	torch.cuda.IntTensor
64-bit integer (signed)	torch.LongTensor	torch.cuda.LongTensor
Boolean	torch.BoolTensor	torch.cuda.BoolTensor

However, to construct tensors, we recommend using factory functions such as torch.empty() with the dtype argument instead. The torch.Tensor constructor is an alias for the default tensor type (torch.FloatTensor).

Initializing and basic operations

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')

For more information about building Tensors, see Creation Ops

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

For more information about indexing, see Indexing, Slicing, Joining, Mutating Ops

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.

Tensor class reference

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.

• There is a legacy constructor torch.Tensor whose use is discouraged. Use torch.tensor() instead.

Tensor.__init__(self, data)

This constructor is deprecated, we recommend using torch.tensor() instead. What this constructor does depends on the type of data.

• If data is a Tensor, returns an alias to the original Tensor. Unlike torch.tensor(), this tracks autograd and will propagate gradients to the original Tensor. device kwarg is not supported for this data type.

• If data is a sequence or nested sequence, create a tensor of the default dtype (typically torch.float32) whose data is the values in the sequences, performing coercions if necessary. Notably, this differs from torch.tensor() in that this constructor will always construct a float tensor, even if the inputs are all integers.

• If data is a torch.Size, returns an empty tensor of that size.

This constructor does not support explicitly specifying dtype or device of the returned tensor. We recommend using torch.tensor() which provides this functionality.

Args:

data (array_like): The tensor to construct from.

Keyword args:

device (torch.device, optional): the desired device of returned tensor.

Default: if None, same torch.device as this tensor.

Tensor.T

Returns a view of 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).

Warning

The use of Tensor.T() on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider mT to transpose batches of matrices or x.permute(*torch.arange(x.ndim - 1, -1, -1)) to reverse the dimensions of a tensor.

Tensor.H

Returns a view of a matrix (2-D tensor) conjugated and transposed.

x.H is equivalent to x.transpose(0, 1).conj() for complex matrices and x.transpose(0, 1) for real matrices.

See also

mH: An attribute that also works on batches of matrices.

Tensor.mT

Returns a view of this tensor with the last two dimensions transposed.

x.mT is equivalent to x.transpose(-2, -1).

Tensor.mH

Accessing this property is equivalent to calling adjoint().

Tensor.new_tensor	Returns a new Tensor with data as the tensor data.
Tensor.new_full	Returns a Tensor of size size filled with fill_value.
Tensor.new_empty	Returns a Tensor of size size filled with uninitialized data.
Tensor.new_ones	Returns a Tensor of size size filled with 1.
Tensor.new_zeros	Returns a Tensor of size size filled with 0.
Tensor.is_cuda	Is True if the Tensor is stored on the GPU, False otherwise.
Tensor.is_quantized	Is True if the Tensor is quantized, False otherwise.
Tensor.is_meta	Is True if the Tensor is a meta tensor, False otherwise.
Tensor.device	Is the torch.device where this Tensor is.
Tensor.grad	This attribute is None by default and becomes a Tensor the first time a call to backward() computes gradients for self.
Tensor.ndim	Alias for dim()
Tensor.real	Returns a new tensor containing real values of the self tensor for a complex-valued input tensor.
Tensor.imag	Returns a new tensor containing imaginary values of the self tensor.
Tensor.nbytes	Returns the number of bytes consumed by the "view" of elements of the Tensor if the Tensor does not use sparse storage layout.
Tensor.itemsize	Alias for element_size()
Tensor.abs	See torch.abs()
Tensor.abs_	In-place version of abs()
Tensor.absolute	Alias for abs()
Tensor.absolute_	In-place version of absolute() Alias for abs_()
Tensor.acos	See torch.acos()
Tensor.acos_	In-place version of acos()
Tensor.arccos	See torch.arccos()
Tensor.arccos_	In-place version of arccos()
Tensor.add	Add a scalar or tensor to self tensor.
Tensor.add_	In-place version of add()
Tensor.addbmm	See torch.addbmm()
Tensor.addbmm_	In-place version of addbmm()
Tensor.addcdiv	See torch.addcdiv()
Tensor.addcdiv_	In-place version of addcdiv()
Tensor.addcmul	See torch.addcmul()
Tensor.addcmul_	In-place version of addcmul()
Tensor.addmm	See torch.addmm()
Tensor.addmm_	In-place version of addmm()
Tensor.sspaddmm	See torch.sspaddmm()
Tensor.addmv	See torch.addmv()
Tensor.addmv_	In-place version of addmv()
Tensor.addr	See torch.addr()
Tensor.addr_	In-place version of addr()
Tensor.adjoint	Alias for adjoint()
Tensor.allclose	See torch.allclose()
Tensor.amax	See torch.amax()
Tensor.amin	See torch.amin()
Tensor.aminmax	See torch.aminmax()
Tensor.angle	See torch.angle()
Tensor.apply_	Applies the function callable to each element in the tensor, replacing each element with the value returned by callable.
Tensor.argmax	See torch.argmax()
Tensor.argmin	See torch.argmin()
Tensor.argsort	See torch.argsort()
Tensor.argwhere	See torch.argwhere()
Tensor.asin	See torch.asin()
Tensor.asin_	In-place version of asin()
Tensor.arcsin	See torch.arcsin()
Tensor.arcsin_	In-place version of arcsin()
Tensor.as_strided	See torch.as_strided()
Tensor.atan	See torch.atan()
Tensor.atan_	In-place version of atan()
Tensor.arctan	See torch.arctan()
Tensor.arctan_	In-place version of arctan()
Tensor.atan2	See torch.atan2()
Tensor.atan2_	In-place version of atan2()
Tensor.arctan2	See torch.arctan2()
Tensor.arctan2_	atan2_(other) -> Tensor
Tensor.all	See torch.all()
Tensor.any	See torch.any()
Tensor.backward	Computes the gradient of current tensor wrt graph leaves.
Tensor.baddbmm	See torch.baddbmm()
Tensor.baddbmm_	In-place version of baddbmm()
Tensor.bernoulli	Returns a result tensor where each $\texttt{result[i]}$ is independently sampled from $\text{Bernoulli}(\texttt{self[i]})$.
Tensor.bernoulli_	Fills each location of self with an independent sample from $\text{Bernoulli}(\texttt{p})$.
Tensor.bfloat16	self.bfloat16() is equivalent to self.to(torch.bfloat16).
Tensor.bincount	See torch.bincount()
Tensor.bitwise_not	See torch.bitwise_not()
Tensor.bitwise_not_	In-place version of bitwise_not()
Tensor.bitwise_and	See torch.bitwise_and()
Tensor.bitwise_and_	In-place version of bitwise_and()
Tensor.bitwise_or	See torch.bitwise_or()
Tensor.bitwise_or_	In-place version of bitwise_or()
Tensor.bitwise_xor	See torch.bitwise_xor()
Tensor.bitwise_xor_	In-place version of bitwise_xor()
Tensor.bitwise_left_shift	See torch.bitwise_left_shift()
Tensor.bitwise_left_shift_	In-place version of bitwise_left_shift()
Tensor.bitwise_right_shift	See torch.bitwise_right_shift()
Tensor.bitwise_right_shift_	In-place version of bitwise_right_shift()
Tensor.bmm	See torch.bmm()
Tensor.bool	self.bool() is equivalent to self.to(torch.bool).
Tensor.byte	self.byte() is equivalent to self.to(torch.uint8).
Tensor.broadcast_to	See torch.broadcast_to().
Tensor.cauchy_	Fills the tensor with numbers drawn from the Cauchy distribution:
Tensor.ceil	See torch.ceil()
Tensor.ceil_	In-place version of ceil()
Tensor.char	self.char() is equivalent to self.to(torch.int8).
Tensor.cholesky	See torch.cholesky()
Tensor.cholesky_inverse	See torch.cholesky_inverse()
Tensor.cholesky_solve	See torch.cholesky_solve()
Tensor.chunk	See torch.chunk()
Tensor.clamp	See torch.clamp()
Tensor.clamp_	In-place version of clamp()
Tensor.clip	Alias for clamp().
Tensor.clip_	Alias for clamp_().
Tensor.clone	See torch.clone()
Tensor.contiguous	Returns a contiguous in memory tensor containing the same data as self tensor.
Tensor.copy_	Copies the elements from src into self tensor and returns self.
Tensor.conj	See torch.conj()
Tensor.conj_physical	See torch.conj_physical()
Tensor.conj_physical_	In-place version of conj_physical()
Tensor.resolve_conj	See torch.resolve_conj()
Tensor.resolve_neg	See torch.resolve_neg()
Tensor.copysign	See torch.copysign()
Tensor.copysign_	In-place version of copysign()
Tensor.cos	See torch.cos()
Tensor.cos_	In-place version of cos()
Tensor.cosh	See torch.cosh()
Tensor.cosh_	In-place version of cosh()
Tensor.corrcoef	See torch.corrcoef()
Tensor.count_nonzero	See torch.count_nonzero()
Tensor.cov	See torch.cov()
Tensor.acosh	See torch.acosh()
Tensor.acosh_	In-place version of acosh()
Tensor.arccosh	acosh() -> Tensor
Tensor.arccosh_	acosh_() -> Tensor
Tensor.cpu	Returns a copy of this object in CPU memory.
Tensor.cross	See torch.cross()
Tensor.cuda	Returns a copy of this object in CUDA memory.
Tensor.logcumsumexp	See torch.logcumsumexp()
Tensor.cummax	See torch.cummax()
Tensor.cummin	See torch.cummin()
Tensor.cumprod	See torch.cumprod()
Tensor.cumprod_	In-place version of cumprod()
Tensor.cumsum	See torch.cumsum()
Tensor.cumsum_	In-place version of cumsum()
Tensor.chalf	self.chalf() is equivalent to self.to(torch.complex32).
Tensor.cfloat	self.cfloat() is equivalent to self.to(torch.complex64).
Tensor.cdouble	self.cdouble() is equivalent to self.to(torch.complex128).
Tensor.data_ptr	Returns the address of the first element of self tensor.
Tensor.deg2rad	See torch.deg2rad()
Tensor.dequantize	Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
Tensor.det	See torch.det()
Tensor.dense_dim	Return the number of dense dimensions in a sparse tensor self.
Tensor.detach	Returns a new Tensor, detached from the current graph.
Tensor.detach_	Detaches the Tensor from the graph that created it, making it a leaf.
Tensor.diag	See torch.diag()
Tensor.diag_embed	See torch.diag_embed()
Tensor.diagflat	See torch.diagflat()
Tensor.diagonal	See torch.diagonal()
Tensor.diagonal_scatter	See torch.diagonal_scatter()
Tensor.fill_diagonal_	Fill the main diagonal of a tensor that has at least 2-dimensions.
Tensor.fmax	See torch.fmax()
Tensor.fmin	See torch.fmin()
Tensor.diff	See torch.diff()
Tensor.digamma	See torch.digamma()
Tensor.digamma_	In-place version of digamma()
Tensor.dim	Returns the number of dimensions of self tensor.
Tensor.dim_order	Returns the uniquely determined tuple of int describing the dim order or physical layout of self.
Tensor.dist	See torch.dist()
Tensor.div	See torch.div()
Tensor.div_	In-place version of div()
Tensor.divide	See torch.divide()
Tensor.divide_	In-place version of divide()
Tensor.dot	See torch.dot()
Tensor.double	self.double() is equivalent to self.to(torch.float64).
Tensor.dsplit	See torch.dsplit()
Tensor.element_size	Returns the size in bytes of an individual element.
Tensor.eq	See torch.eq()
Tensor.eq_	In-place version of eq()
Tensor.equal	See torch.equal()
Tensor.erf	See torch.erf()
Tensor.erf_	In-place version of erf()
Tensor.erfc	See torch.erfc()
Tensor.erfc_	In-place version of erfc()
Tensor.erfinv	See torch.erfinv()
Tensor.erfinv_	In-place version of erfinv()
Tensor.exp	See torch.exp()
Tensor.exp_	In-place version of exp()
Tensor.expm1	See torch.expm1()
Tensor.expm1_	In-place version of expm1()
Tensor.expand	Returns a new view of the self tensor with singleton dimensions expanded to a larger size.
Tensor.expand_as	Expand this tensor to the same size as other.
Tensor.exponential_	Fills self tensor with elements drawn from the PDF (probability density function):
Tensor.fix	See torch.fix().
Tensor.fix_	In-place version of fix()
Tensor.fill_	Fills self tensor with the specified value.
Tensor.flatten	See torch.flatten()
Tensor.flip	See torch.flip()
Tensor.fliplr	See torch.fliplr()
Tensor.flipud	See torch.flipud()
Tensor.float	self.float() is equivalent to self.to(torch.float32).
Tensor.float_power	See torch.float_power()
Tensor.float_power_	In-place version of float_power()
Tensor.floor	See torch.floor()
Tensor.floor_	In-place version of floor()
Tensor.floor_divide	See torch.floor_divide()
Tensor.floor_divide_	In-place version of floor_divide()
Tensor.fmod	See torch.fmod()
Tensor.fmod_	In-place version of fmod()
Tensor.frac	See torch.frac()
Tensor.frac_	In-place version of frac()
Tensor.frexp	See torch.frexp()
Tensor.gather	See torch.gather()
Tensor.gcd	See torch.gcd()
Tensor.gcd_	In-place version of gcd()
Tensor.ge	See torch.ge().
Tensor.ge_	In-place version of ge().
Tensor.greater_equal	See torch.greater_equal().
Tensor.greater_equal_	In-place version of greater_equal().
Tensor.geometric_	Fills self tensor with elements drawn from the geometric distribution:
Tensor.geqrf	See torch.geqrf()
Tensor.ger	See torch.ger()
Tensor.get_device	For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides.
Tensor.gt	See torch.gt().
Tensor.gt_	In-place version of gt().
Tensor.greater	See torch.greater().
Tensor.greater_	In-place version of greater().
Tensor.half	self.half() is equivalent to self.to(torch.float16).
Tensor.hardshrink	See torch.nn.functional.hardshrink()
Tensor.heaviside	See torch.heaviside()
Tensor.histc	See torch.histc()
Tensor.histogram	See torch.histogram()
Tensor.hsplit	See torch.hsplit()
Tensor.hypot	See torch.hypot()
Tensor.hypot_	In-place version of hypot()
Tensor.i0	See torch.i0()
Tensor.i0_	In-place version of i0()
Tensor.igamma	See torch.igamma()
Tensor.igamma_	In-place version of igamma()
Tensor.igammac	See torch.igammac()
Tensor.igammac_	In-place version of igammac()
Tensor.index_add_	Accumulate the elements of alpha times source into the self tensor by adding to the indices in the order given in index.
Tensor.index_add	Out-of-place version of torch.Tensor.index_add_().
Tensor.index_copy_	Copies the elements of tensor into the self tensor by selecting the indices in the order given in index.
Tensor.index_copy	Out-of-place version of torch.Tensor.index_copy_().
Tensor.index_fill_	Fills the elements of the self tensor with value value by selecting the indices in the order given in index.
Tensor.index_fill	Out-of-place version of torch.Tensor.index_fill_().
Tensor.index_put_	Puts values from the tensor values into the tensor self using the indices specified in indices (which is a tuple of Tensors).
Tensor.index_put	Out-place version of index_put_().
Tensor.index_reduce_	Accumulate the elements of source into the self tensor by accumulating to the indices in the order given in index using the reduction given by the reduce argument.
Tensor.index_reduce
Tensor.index_select	See torch.index_select()
Tensor.indices	Return the indices tensor of a sparse COO tensor.
Tensor.inner	See torch.inner().
Tensor.int	self.int() is equivalent to self.to(torch.int32).
Tensor.int_repr	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.
Tensor.inverse	See torch.inverse()
Tensor.isclose	See torch.isclose()
Tensor.isfinite	See torch.isfinite()
Tensor.isinf	See torch.isinf()
Tensor.isposinf	See torch.isposinf()
Tensor.isneginf	See torch.isneginf()
Tensor.isnan	See torch.isnan()
Tensor.is_contiguous	Returns True if self tensor is contiguous in memory in the order specified by memory format.
Tensor.is_complex	Returns True if the data type of self is a complex data type.
Tensor.is_conj	Returns True if the conjugate bit of self is set to true.
Tensor.is_floating_point	Returns True if the data type of self is a floating point data type.
Tensor.is_inference	See torch.is_inference()
Tensor.is_leaf	All Tensors that have requires_grad which is False will be leaf Tensors by convention.
Tensor.is_pinned	Returns true if this tensor resides in pinned memory.
Tensor.is_set_to	Returns True if both tensors are pointing to the exact same memory (same storage, offset, size and stride).
Tensor.is_shared	Checks if tensor is in shared memory.
Tensor.is_signed	Returns True if the data type of self is a signed data type.
Tensor.is_sparse	Is True if the Tensor uses sparse COO storage layout, False otherwise.
Tensor.istft	See torch.istft()
Tensor.isreal	See torch.isreal()
Tensor.item	Returns the value of this tensor as a standard Python number.
Tensor.kthvalue	See torch.kthvalue()
Tensor.lcm	See torch.lcm()
Tensor.lcm_	In-place version of lcm()
Tensor.ldexp	See torch.ldexp()
Tensor.ldexp_	In-place version of ldexp()
Tensor.le	See torch.le().
Tensor.le_	In-place version of le().
Tensor.less_equal	See torch.less_equal().
Tensor.less_equal_	In-place version of less_equal().
Tensor.lerp	See torch.lerp()
Tensor.lerp_	In-place version of lerp()
Tensor.lgamma	See torch.lgamma()
Tensor.lgamma_	In-place version of lgamma()
Tensor.log	See torch.log()
Tensor.log_	In-place version of log()
Tensor.logdet	See torch.logdet()
Tensor.log10	See torch.log10()
Tensor.log10_	In-place version of log10()
Tensor.log1p	See torch.log1p()
Tensor.log1p_	In-place version of log1p()
Tensor.log2	See torch.log2()
Tensor.log2_	In-place version of log2()
Tensor.log_normal_	Fills self tensor with numbers samples from the log-normal distribution parameterized by the given mean $\mu$ and standard deviation $\sigma$.
Tensor.logaddexp	See torch.logaddexp()
Tensor.logaddexp2	See torch.logaddexp2()
Tensor.logsumexp	See torch.logsumexp()
Tensor.logical_and	See torch.logical_and()
Tensor.logical_and_	In-place version of logical_and()
Tensor.logical_not	See torch.logical_not()
Tensor.logical_not_	In-place version of logical_not()
Tensor.logical_or	See torch.logical_or()
Tensor.logical_or_	In-place version of logical_or()
Tensor.logical_xor	See torch.logical_xor()
Tensor.logical_xor_	In-place version of logical_xor()
Tensor.logit	See torch.logit()
Tensor.logit_	In-place version of logit()
Tensor.long	self.long() is equivalent to self.to(torch.int64).
Tensor.lt	See torch.lt().
Tensor.lt_	In-place version of lt().
Tensor.less	lt(other) -> Tensor
Tensor.less_	In-place version of less().
Tensor.lu	See torch.lu()
Tensor.lu_solve	See torch.lu_solve()
Tensor.as_subclass	Makes a cls instance with the same data pointer as self.
Tensor.map_	Applies callable for each element in self tensor and the given tensor and stores the results in self tensor.
Tensor.masked_scatter_	Copies elements from source into self tensor at positions where the mask is True.
Tensor.masked_scatter	Out-of-place version of torch.Tensor.masked_scatter_()
Tensor.masked_fill_	Fills elements of self tensor with value where mask is True.
Tensor.masked_fill	Out-of-place version of torch.Tensor.masked_fill_()
Tensor.masked_select	See torch.masked_select()
Tensor.matmul	See torch.matmul()
Tensor.matrix_power	Note matrix_power() is deprecated, use torch.linalg.matrix_power() instead.
Tensor.matrix_exp	See torch.matrix_exp()
Tensor.max	See torch.max()
Tensor.maximum	See torch.maximum()
Tensor.mean	See torch.mean()
Tensor.module_load	Defines how to transform other when loading it into self in load_state_dict().
Tensor.nanmean	See torch.nanmean()
Tensor.median	See torch.median()
Tensor.nanmedian	See torch.nanmedian()
Tensor.min	See torch.min()
Tensor.minimum	See torch.minimum()
Tensor.mm	See torch.mm()
Tensor.smm	See torch.smm()
Tensor.mode	See torch.mode()
Tensor.movedim	See torch.movedim()
Tensor.moveaxis	See torch.moveaxis()
Tensor.msort	See torch.msort()
Tensor.mul	See torch.mul().
Tensor.mul_	In-place version of mul().
Tensor.multiply	See torch.multiply().
Tensor.multiply_	In-place version of multiply().
Tensor.multinomial	See torch.multinomial()
Tensor.mv	See torch.mv()
Tensor.mvlgamma	See torch.mvlgamma()
Tensor.mvlgamma_	In-place version of mvlgamma()
Tensor.nansum	See torch.nansum()
Tensor.narrow	See torch.narrow().
Tensor.narrow_copy	See torch.narrow_copy().
Tensor.ndimension	Alias for dim()
Tensor.nan_to_num	See torch.nan_to_num().
Tensor.nan_to_num_	In-place version of nan_to_num().
Tensor.ne	See torch.ne().
Tensor.ne_	In-place version of ne().
Tensor.not_equal	See torch.not_equal().
Tensor.not_equal_	In-place version of not_equal().
Tensor.neg	See torch.neg()
Tensor.neg_	In-place version of neg()
Tensor.negative	See torch.negative()
Tensor.negative_	In-place version of negative()
Tensor.nelement	Alias for numel()
Tensor.nextafter	See torch.nextafter()
Tensor.nextafter_	In-place version of nextafter()
Tensor.nonzero	See torch.nonzero()
Tensor.norm	See torch.norm()
Tensor.normal_	Fills self tensor with elements samples from the normal distribution parameterized by mean and std.
Tensor.numel	See torch.numel()
Tensor.numpy	Returns the tensor as a NumPy ndarray.
Tensor.orgqr	See torch.orgqr()
Tensor.ormqr	See torch.ormqr()
Tensor.outer	See torch.outer().
Tensor.permute	See torch.permute()
Tensor.pin_memory	Copies the tensor to pinned memory, if it's not already pinned.
Tensor.pinverse	See torch.pinverse()
Tensor.polygamma	See torch.polygamma()
Tensor.polygamma_	In-place version of polygamma()
Tensor.positive	See torch.positive()
Tensor.pow	See torch.pow()
Tensor.pow_	In-place version of pow()
Tensor.prod	See torch.prod()
Tensor.put_	Copies the elements from source into the positions specified by index.
Tensor.qr	See torch.qr()
Tensor.qscheme	Returns the quantization scheme of a given QTensor.
Tensor.quantile	See torch.quantile()
Tensor.nanquantile	See torch.nanquantile()
Tensor.q_scale	Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().
Tensor.q_zero_point	Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().
Tensor.q_per_channel_scales	Given a Tensor quantized by linear (affine) per-channel quantization, returns a Tensor of scales of the underlying quantizer.
Tensor.q_per_channel_zero_points	Given a Tensor quantized by linear (affine) per-channel quantization, returns a tensor of zero_points of the underlying quantizer.
Tensor.q_per_channel_axis	Given a Tensor quantized by linear (affine) per-channel quantization, returns the index of dimension on which per-channel quantization is applied.
Tensor.rad2deg	See torch.rad2deg()
Tensor.random_	Fills self tensor with numbers sampled from the discrete uniform distribution over [from, to - 1].
Tensor.ravel	see torch.ravel()
Tensor.reciprocal	See torch.reciprocal()
Tensor.reciprocal_	In-place version of reciprocal()
Tensor.record_stream	Marks the tensor as having been used by this stream.
Tensor.register_hook	Registers a backward hook.
Tensor.register_post_accumulate_grad_hook	Registers a backward hook that runs after grad accumulation.
Tensor.remainder	See torch.remainder()
Tensor.remainder_	In-place version of remainder()
Tensor.renorm	See torch.renorm()
Tensor.renorm_	In-place version of renorm()
Tensor.repeat	Repeats this tensor along the specified dimensions.
Tensor.repeat_interleave	See torch.repeat_interleave().
Tensor.requires_grad	Is True if gradients need to be computed for this Tensor, False otherwise.
Tensor.requires_grad_	Change if autograd should record operations on this tensor: sets this tensor's requires_grad attribute in-place.
Tensor.reshape	Returns a tensor with the same data and number of elements as self but with the specified shape.
Tensor.reshape_as	Returns this tensor as the same shape as other.
Tensor.resize_	Resizes self tensor to the specified size.
Tensor.resize_as_	Resizes the self tensor to be the same size as the specified tensor.
Tensor.retain_grad	Enables this Tensor to have their grad populated during backward().
Tensor.retains_grad	Is True if this Tensor is non-leaf and its grad is enabled to be populated during backward(), False otherwise.
Tensor.roll	See torch.roll()
Tensor.rot90	See torch.rot90()
Tensor.round	See torch.round()
Tensor.round_	In-place version of round()
Tensor.rsqrt	See torch.rsqrt()
Tensor.rsqrt_	In-place version of rsqrt()
Tensor.scatter	Out-of-place version of torch.Tensor.scatter_()
Tensor.scatter_	Writes all values from the tensor src into self at the indices specified in the index tensor.
Tensor.scatter_add_	Adds all values from the tensor src into self at the indices specified in the index tensor in a similar fashion as scatter_().
Tensor.scatter_add	Out-of-place version of torch.Tensor.scatter_add_()
Tensor.scatter_reduce_	Reduces all values from the src tensor to the indices specified in the index tensor in the self tensor using the applied reduction defined via the reduce argument ("sum", "prod", "mean", "amax", "amin").
Tensor.scatter_reduce	Out-of-place version of torch.Tensor.scatter_reduce_()
Tensor.select	See torch.select()
Tensor.select_scatter	See torch.select_scatter()
Tensor.set_	Sets the underlying storage, size, and strides.
Tensor.share_memory_	Moves the underlying storage to shared memory.
Tensor.short	self.short() is equivalent to self.to(torch.int16).
Tensor.sigmoid	See torch.sigmoid()
Tensor.sigmoid_	In-place version of sigmoid()
Tensor.sign	See torch.sign()
Tensor.sign_	In-place version of sign()
Tensor.signbit	See torch.signbit()
Tensor.sgn	See torch.sgn()
Tensor.sgn_	In-place version of sgn()
Tensor.sin	See torch.sin()
Tensor.sin_	In-place version of sin()
Tensor.sinc	See torch.sinc()
Tensor.sinc_	In-place version of sinc()
Tensor.sinh	See torch.sinh()
Tensor.sinh_	In-place version of sinh()
Tensor.asinh	See torch.asinh()
Tensor.asinh_	In-place version of asinh()
Tensor.arcsinh	See torch.arcsinh()
Tensor.arcsinh_	In-place version of arcsinh()
Tensor.shape	Returns the size of the self tensor.
Tensor.size	Returns the size of the self tensor.
Tensor.slogdet	See torch.slogdet()
Tensor.slice_scatter	See torch.slice_scatter()
Tensor.softmax	Alias for torch.nn.functional.softmax().
Tensor.sort	See torch.sort()
Tensor.split	See torch.split()
Tensor.sparse_mask	Returns a new sparse tensor with values from a strided tensor self filtered by the indices of the sparse tensor mask.
Tensor.sparse_dim	Return the number of sparse dimensions in a sparse tensor self.
Tensor.sqrt	See torch.sqrt()
Tensor.sqrt_	In-place version of sqrt()
Tensor.square	See torch.square()
Tensor.square_	In-place version of square()
Tensor.squeeze	See torch.squeeze()
Tensor.squeeze_	In-place version of squeeze()
Tensor.std	See torch.std()
Tensor.stft	See torch.stft()
Tensor.storage	Returns the underlying TypedStorage.
Tensor.untyped_storage	Returns the underlying UntypedStorage.
Tensor.storage_offset	Returns self tensor's offset in the underlying storage in terms of number of storage elements (not bytes).
Tensor.storage_type	Returns the type of the underlying storage.
Tensor.stride	Returns the stride of self tensor.
Tensor.sub	See torch.sub().
Tensor.sub_	In-place version of sub()
Tensor.subtract	See torch.subtract().
Tensor.subtract_	In-place version of subtract().
Tensor.sum	See torch.sum()
Tensor.sum_to_size	Sum this tensor to size.
Tensor.svd	See torch.svd()
Tensor.swapaxes	See torch.swapaxes()
Tensor.swapdims	See torch.swapdims()
Tensor.t	See torch.t()
Tensor.t_	In-place version of t()
Tensor.tensor_split	See torch.tensor_split()
Tensor.tile	See torch.tile()
Tensor.to	Performs Tensor dtype and/or device conversion.
Tensor.to_mkldnn	Returns a copy of the tensor in torch.mkldnn layout.
Tensor.take	See torch.take()
Tensor.take_along_dim	See torch.take_along_dim()
Tensor.tan	See torch.tan()
Tensor.tan_	In-place version of tan()
Tensor.tanh	See torch.tanh()
Tensor.tanh_	In-place version of tanh()
Tensor.atanh	See torch.atanh()
Tensor.atanh_	In-place version of atanh()
Tensor.arctanh	See torch.arctanh()
Tensor.arctanh_	In-place version of arctanh()
Tensor.tolist	Returns the tensor as a (nested) list.
Tensor.topk	See torch.topk()
Tensor.to_dense	Creates a strided copy of self if self is not a strided tensor, otherwise returns self.
Tensor.to_sparse	Returns a sparse copy of the tensor.
Tensor.to_sparse_csr	Convert a tensor to compressed row storage format (CSR).
Tensor.to_sparse_csc	Convert a tensor to compressed column storage (CSC) format.
Tensor.to_sparse_bsr	Convert a tensor to a block sparse row (BSR) storage format of given blocksize.
Tensor.to_sparse_bsc	Convert a tensor to a block sparse column (BSC) storage format of given blocksize.
Tensor.trace	See torch.trace()
Tensor.transpose	See torch.transpose()
Tensor.transpose_	In-place version of transpose()
Tensor.triangular_solve	See torch.triangular_solve()
Tensor.tril	See torch.tril()
Tensor.tril_	In-place version of tril()
Tensor.triu	See torch.triu()
Tensor.triu_	In-place version of triu()
Tensor.true_divide	See torch.true_divide()
Tensor.true_divide_	In-place version of true_divide_()
Tensor.trunc	See torch.trunc()
Tensor.trunc_	In-place version of trunc()
Tensor.type	Returns the type if dtype is not provided, else casts this object to the specified type.
Tensor.type_as	Returns this tensor cast to the type of the given tensor.
Tensor.unbind	See torch.unbind()
Tensor.unflatten	See torch.unflatten().
Tensor.unfold	Returns a view of the original tensor which contains all slices of size size from self tensor in the dimension dimension.
Tensor.uniform_	Fills self tensor with numbers sampled from the continuous uniform distribution:
Tensor.unique	Returns the unique elements of the input tensor.
Tensor.unique_consecutive	Eliminates all but the first element from every consecutive group of equivalent elements.
Tensor.unsqueeze	See torch.unsqueeze()
Tensor.unsqueeze_	In-place version of unsqueeze()
Tensor.values	Return the values tensor of a sparse COO tensor.
Tensor.var	See torch.var()
Tensor.vdot	See torch.vdot()
Tensor.view	Returns a new tensor with the same data as the self tensor but of a different shape.
Tensor.view_as	View this tensor as the same size as other.
Tensor.vsplit	See torch.vsplit()
Tensor.where	self.where(condition, y) is equivalent to torch.where(condition, self, y).
Tensor.xlogy	See torch.xlogy()
Tensor.xlogy_	In-place version of xlogy()
Tensor.xpu	Returns a copy of this object in XPU memory.
Tensor.zero_	Fills self tensor with zeros.