Central to torch is the `torch_tensor`

objects.
`torch_tensor`

’s are R objects very similar to R6 instances.
Tensors have a large amount of methods that can be called using the
`$`

operator.

Following is a list of all methods that can be called by tensor objects and their documentation. You can also look at PyTorch’s documentation for additional details.

## numpy_T

Is this Tensor with its dimensions reversed.

If `n`

is the number of dimensions in `x`

,
`x$numpy_T()`

is equivalent to
`x$permute(n, n-1, ..., 1)`

.

## add

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`

## align_as

align_as(other) -> Tensor

Permutes the dimensions of the `self`

tensor to match the
dimension order in the `other`

tensor, adding size-one dims
for any new names.

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

All of the dims of `self`

must be named in order to use
this method. The resulting tensor is a view on the original tensor.

All dimension names of `self`

must be present in
`other$names`

. `other`

may contain named
dimensions that are not in `self$names`

; the output tensor
has a size-one dimension for each of those new names.

To align a tensor to a specific order, use
`$align_to`

.

### Examples:

```
# Example 1: Applying a mask
mask <- torch_randint(low = 0, high = 2, size = c(127, 128), dtype=torch_bool())$refine_names(c('W', 'H'))
imgs <- torch_randn(32, 128, 127, 3, names=c('N', 'H', 'W', 'C'))
imgs$masked_fill_(mask$align_as(imgs), 0)
# Example 2: Applying a per-channel-scale
scale_channels <- function(input, scale) {
scale <- scale$refine_names("C")
input * scale$align_as(input)
}
num_channels <- 3
scale <- torch_randn(num_channels, names='C')
imgs <- torch_rand(32, 128, 128, num_channels, names=c('N', 'H', 'W', 'C'))
more_imgs = torch_rand(32, num_channels, 128, 128, names=c('N', 'C', 'H', 'W'))
videos = torch_randn(3, num_channels, 128, 128, 128, names=c('N', 'C', 'H', 'W', 'D'))
# scale_channels is agnostic to the dimension order of the input
scale_channels(imgs, scale)
scale_channels(more_imgs, scale)
scale_channels(videos, scale)
```

## align_to

Permutes the dimensions of the `self`

tensor to match the
order specified in `names`

, adding size-one dims for any new
names.

All of the dims of `self`

must be named in order to use
this method. The resulting tensor is a view on the original tensor.

All dimension names of `self`

must be present in
`names`

. `names`

may contain additional names that
are not in `self$names`

; the output tensor has a size-one
dimension for each of those new names.

## all

all() -> bool

Returns TRUE if all elements in the tensor are TRUE, FALSE otherwise.

#### Examples:

```
a <- torch_rand(1, 2)$to(dtype = torch_bool())
a
a$all()
```

all(dim, keepdim=FALSE, out=NULL) -> Tensor

Returns TRUE if all elements in each row of the tensor in the given
dimension `dim`

are TRUE, FALSE otherwise.

If `keepdim`

is `TRUE`

, the output tensor is of
the same size as `input`

except in the dimension
`dim`

where it is of size 1. Otherwise, `dim`

is
squeezed (see `?torch_squeeze()),`

resulting in the output
tensor having 1 fewer dimension than `input`

.

#### Arguments:

- dim (int): the dimension to reduce
- keepdim (bool): whether the output tensor has
`dim`

retained or not - out (Tensor, optional): the output tensor

#### Examples:

```
a <- torch_rand(4, 2)$to(dtype = torch_bool())
a
a$all(dim=2)
a$all(dim=1)
```

## any

any() -> bool

Returns TRUE if any elements in the tensor are TRUE, FALSE otherwise.

#### Examples:

```
a <- torch_rand(1, 2)$to(dtype = torch_bool())
a
a$any()
```

any(dim, keepdim=FALSE, out=NULL) -> Tensor

Returns TRUE if any elements in each row of the tensor in the given
dimension `dim`

are TRUE, FALSE otherwise.

If `keepdim`

is `TRUE`

, the output tensor is of
the same size as `input`

except in the dimension
`dim`

where it is of size 1. Otherwise, `dim`

is
squeezed (see `?torch_squeeze()),`

resulting in the output
tensor having 1 fewer dimension than `input`

.

#### Arguments:

- dim (int): the dimension to reduce
- keepdim (bool): whether the output tensor has
`dim`

retained or not - out (Tensor, optional): the output tensor

#### Examples:

```
a <- torch_randn(4, 2) < 0
a
a$any(2)
a$any(1)
```

## apply_

apply_(callable) -> Tensor

Applies the function `callable`

to each element in the
tensor, replacing each element with the value returned by
`callable`

.

## as_subclass

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`

.

## backward

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 `NULL`

before calling it. See
`Default gradient layouts<default-grad-layouts>`

for
details on the memory layout of accumulated gradients.

#### Arguments:

- gradient (Tensor or NULL): 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. NULL values can be specified for scalar Tensors or ones that don’t require grad. If a NULL 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`

.

## bernoulli

bernoulli(*, generator=NULL) -> Tensor

Returns a result tensor where each \(\texttt{result[i]}\) is independently
sampled from \(\text{Bernoulli}(\texttt{self[i]})\).
`self`

must have floating point `dtype`

, and the
result will have the same `dtype`

.

See `?torch_bernoulli`

## bernoulli_

bernoulli_(p=0.5, *, generator=NULL) -> Tensor

Fills each location of `self`

with an independent sample
from \(\text{Bernoulli}(\texttt{p})\).
`self`

can have integral `dtype`

.

bernoulli_(p_tensor, *, generator=NULL) -> Tensor

`p_tensor`

should be a tensor containing probabilities to
be used for drawing the binary random number.

The \(\text{i}^{th}\) element of
`self`

tensor will be set to a value sampled from \(\text{Bernoulli}(\texttt{p\_tensor[i]})\).

`self`

can have integral `dtype`

, but
`p_tensor`

must have floating point `dtype`

.

See also `$bernoulli`

and
`?torch_bernoulli`

## bfloat16

bfloat16(memory_format=torch_preserve_format) -> Tensor
`self$bfloat16()`

is equivalent to
`self$to(torch_bfloat16)`

. See [to()].

## bool

bool(memory_format=torch_preserve_format) -> Tensor

`self$bool()`

is equivalent to
`self$to(torch_bool)`

. See [to()].

## byte

byte(memory_format=torch_preserve_format) -> Tensor

`self$byte()`

is equivalent to
`self$to(torch_uint8)`

. See [to()].

## cauchy_

cauchy_(median=0, sigma=1, *, generator=NULL) -> Tensor

Fills the tensor with numbers drawn from the Cauchy distribution:

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

## char

char(memory_format=torch_preserve_format) -> Tensor

`self$char()`

is equivalent to
`self$to(torch_int8)`

. See [to()].

## clone

clone(memory_format=torch_preserve_format()) -> Tensor

Returns a copy of the `self`

tensor. The copy has the same
size and data type as `self`

.

```
x <- torch_tensor(1)
y <- x$clone()
x$add_(1)
y
```

## contiguous

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.

## copy_

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.

## cpu

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.

## cuda

cuda(device=NULL, 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.

#### Arguments:

- 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`

.

## dense_dim

dense_dim() -> int

If `self`

is a sparse COO tensor (i.e., with
`torch_sparse_coo`

layout), this returns the number of dense
dimensions. Otherwise, this throws an error.

See also `$sparse_dim`

.

## dequantize

dequantize() -> Tensor

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

## 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.

## double

double(memory_format=torch_preserve_format) -> Tensor

`self$double()`

is equivalent to
`self$to(torch_float64)`

. See [to()].

## element_size

element_size() -> int

Returns the size in bytes of an individual element.

#### Examples:

`torch_tensor(c(1))$element_size()`

## expand

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.

#### 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.

#### Examples:

```
x <- torch_tensor(matrix(c(1,2,3), ncol = 1))
x$size()
x$expand(c(3, 4))
x$expand(c(-1, 4)) # -1 means not changing the size of that dimension
```

## expand_as

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`

.

## exponential_

exponential_(lambd=1, *, generator=NULL) -> Tensor

Fills `self`

tensor with elements drawn from the
exponential distribution:

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

## fill_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.

#### Arguments:

- fill_value (Scalar): the fill value
- wrap (bool): the diagonal ‘wrapped’ after N columns for tall matrices.

#### Examples:

```
a <- torch_zeros(3, 3)
a$fill_diagonal_(5)
b <- torch_zeros(7, 3)
b$fill_diagonal_(5)
c <- torch_zeros(7, 3)
c$fill_diagonal_(5, wrap=TRUE)
```

## float

float(memory_format=torch_preserve_format) -> Tensor

`self$float()`

is equivalent to
`self$to(torch_float32)`

. See [to()].

## geometric_

geometric_(p, *, generator=NULL) -> Tensor

Fills `self`

tensor with elements drawn from the geometric
distribution:

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

## get_device

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.

#### Examples:

```
x <- torch_randn(3, 4, 5, device='cuda:0')
x$get_device()
x$cpu()$get_device() # RuntimeError: get_device is not implemented for type torch_FloatTensor
```

## grad

This attribute is `NULL`

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.

## half

half(memory_format=torch_preserve_format) -> Tensor

`self$half()`

is equivalent to
`self$to(torch_float16)`

. See [to()].

## imag

Returns a new tensor containing imaginary values of the
`self`

tensor. The returned tensor and `self`

share the same underlying storage.

#### Examples:

```
x <- torch_randn(4, dtype=torch_cfloat())
x
x$imag
```

## index_add

index_add(tensor1, dim, index, tensor2) -> Tensor

Out-of-place version of `$index_add_`

.
`tensor1`

corresponds to `self`

in
`$index_add_`

.

## index_add_

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 `i`

th row of
`tensor`

is added to the `j`

th row of
`self`

.

The `dim`

th 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:

In some circumstances when using the CUDA backend with CuDNN, this
operator may select a nondeterministic algorithm to increase
performance. If this is undesirable, you can try to make the operation
deterministic (potentially at a performance cost) by setting
`torch_backends.cudnn.deterministic = TRUE`

.

#### Arguments:

- dim (int): dimension along which to index
- index (LongTensor): indices of
`tensor`

to select from - tensor (Tensor): the tensor containing values to add

#### Examples:

```
x <- torch_ones(5, 3)
t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
index <- torch_tensor(c(1L, 4L, 3L))
x$index_add_(1, index, t)
```

## index_copy

index_copy(tensor1, dim, index, tensor2) -> Tensor

Out-of-place version of `$index_copy_`

.
`tensor1`

corresponds to `self`

in
`$index_copy_`

.

## index_copy_

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 `i`

th row of
`tensor`

is copied to the `j`

th row of
`self`

.

The `dim`

th 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.

#### Arguments:

- dim (int): dimension along which to index
- index (LongTensor): indices of
`tensor`

to select from - tensor (Tensor): the tensor containing values to copy

#### Examples:

```
x <- torch_zeros(5, 3)
t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
index <- torch_tensor(c(1, 5, 3))
x$index_copy_(1, index, t)
```

## index_fill

index_fill(tensor1, dim, index, value) -> Tensor

Out-of-place version of `$index_fill_`

.
`tensor1`

corresponds to `self`

in
`$index_fill_`

.

## index_fill_

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`

.

#### Arguments:

- dim (int): dimension along which to index
- index (LongTensor): indices of
`self`

tensor to fill in - val (float): the value to fill with

#### Examples:

```
x <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float())
index <- torch_tensor(c(1, 3), dtype = torch_long())
x$index_fill_(1, index, -1)
```

## index_put

index_put(tensor1, indices, value, accumulate=FALSE) -> Tensor

Out-place version of `$index_put_`

. `tensor1`

corresponds to `self`

in `$index_put_`

.

## index_put_

index_put_(indices, value, accumulate=FALSE) -> Tensor

Puts values from the tensor `value`

into the tensor
`self`

using the indices specified in `indices`

(which is a tuple of Tensors). The expression
`tensor.index_put_(indices, value)`

is equivalent to
`tensor[indices] = value`

. Returns `self`

.

If `accumulate`

is `TRUE`

, the elements in
`value`

are added to `self`

. If accumulate is
`FALSE`

, the behavior is undefined if indices contain
duplicate elements.

## indices

indices() -> Tensor

If `self`

is a sparse COO tensor (i.e., with
`torch_sparse_coo`

layout), this returns a view of the
contained indices tensor. Otherwise, this throws an error.

See also `Tensor.values`

.

## int

int(memory_format=torch_preserve_format) -> Tensor

`self$int()`

is equivalent to
`self$to(torch_int32)`

. See [to()].

## int_repr

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.

## irfft

irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor

See `?torch_irfft`

## is_contiguous

is_contiguous(memory_format=torch_contiguous_format) -> bool

Returns TRUE if `self`

tensor is contiguous in memory in
the order specified by memory format.

## is_floating_point

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 NULL.

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()].

#### Examples:

```
a <- torch_rand(10, requires_grad=TRUE)
a$is_leaf
# b <- torch_rand(10, requires_grad=TRUE)$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
# 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_meta

Is `TRUE`

if the Tensor is a meta tensor,
`FALSE`

otherwise. Meta tensors are like normal tensors, but
they carry no data.

## is_set_to

is_set_to(tensor) -> bool

Returns TRUE if this object refers to the same `THTensor`

object from the Torch C API as the given tensor.

## istft

See `?torch_istft`

## item

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.

#### Examples:

```
x <- torch_tensor(1.0)
x$item()
```

## log_normal_

log_normal_(mean=1, std=2, *, generator=NULL)

Fills `self`

tensor with numbers samples from the
log-normal distribution parameterized by the given mean `\mu`

and standard deviation `\sigma`

. Note that `mean`

and `std`

are the mean and standard deviation of the
underlying normal distribution, and not of the returned
distribution:

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

## long

long(memory_format=torch_preserve_format) -> Tensor

`self$long()`

is equivalent to
`self$to(torch_int64)`

. See [to()].

## map_

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:

`callable(a, b) -> number`

## masked_fill_

masked_fill_(mask, value)

Fills elements of `self`

tensor with `value`

where `mask`

is TRUE. The shape of `mask`

must be
`broadcastable <broadcasting-semantics>`

with the shape
of the underlying tensor.

## masked_scatter_

masked_scatter_(mask, source)

Copies elements from `source`

into `self`

tensor at positions where the `mask`

is TRUE. The shape of
`mask`

must be
:ref:`broadcastable <broadcasting-semantics>`

with the
shape of the underlying tensor. The `source`

should have at
least as many elements as the number of ones in `mask`

## names

Stores names for each of this tensor’s dimensions.

`names[idx]`

corresponds to the name of tensor dimension
`idx`

. Names are either a string if the dimension is named or
`NULL`

if the dimension is unnamed.

Dimension names may contain characters or underscore. Furthermore, a dimension name must be a valid Python variable name (i.e., does not start with underscore).

Tensors may not have two named dimensions with the same name.

## narrow

narrow(dimension, start, length) -> Tensor

See `?torch_narrow`

#### Examples:

```
x <- torch_tensor(matrix(1:9, ncol = 3))
x$narrow(1, 1, 3)
x$narrow(1, 1, 2)
```

## narrow_copy

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.

## new_empty

new_empty(size, dtype=NULL, device=NULL, 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.

#### Arguments:

- dtype (
`torch_dtype`

, optional): the desired type of returned tensor. Default: if NULL, same`torch_dtype`

as this tensor. - device (
`torch_device`

, optional): the desired device of returned tensor. Default: if NULL, same`torch_device`

as this tensor. - requires_grad (bool, optional): If autograd should record operations on the
- returned tensor. Default:
`FALSE`

.

#### Examples:

```
tensor <- torch_ones(5)
tensor$new_empty(c(2, 3))
```

## new_full

new_full(size, fill_value, dtype=NULL, device=NULL, 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.

#### Arguments:

- fill_value (scalar): the number to fill the output tensor with.
- dtype (
`torch_dtype`

, optional): the desired type of returned tensor. Default: if NULL, same`torch_dtype`

as this tensor. - device (
`torch_device`

, optional): the desired device of returned tensor. Default: if NULL, same`torch_device`

as this tensor. - requires_grad (bool, optional): If autograd should record operations on the
- returned tensor. Default:
`FALSE`

.

#### Examples:

```
tensor <- torch_ones(c(2), dtype=torch_float64())
tensor$new_full(c(3, 4), 3.141592)
```

## new_ones

new_ones(size, dtype=NULL, device=NULL, 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.

#### Arguments:

- 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 NULL, same`torch_dtype`

as this tensor. - device (
`torch_device`

, optional): the desired device of returned tensor. Default: if NULL, same`torch_device`

as this tensor. - requires_grad (bool, optional): If autograd should record operations on the
- returned tensor. Default:
`FALSE`

.

#### Examples:

```
tensor <- torch_tensor(c(2), dtype=torch_int32())
tensor$new_ones(c(2, 3))
```

## new_tensor

new_tensor(data, dtype=NULL, device=NULL, 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 [\(requires_grad_()]
or [\)detach()]. If you have a numpy array and want to avoid a
copy, use [torch_from_numpy()].

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.

#### Arguments:

- data (array_like): The returned Tensor copies
`data`

. - dtype (
`torch_dtype`

, optional): the desired type of returned tensor. Default: if NULL, same`torch_dtype`

as this tensor. - device (
`torch_device`

, optional): the desired device of returned tensor. Default: if NULL, same`torch_device`

as this tensor. - requires_grad (bool, optional): If autograd should record operations on the
- returned tensor. Default:
`FALSE`

.

#### Examples:

```
tensor <- torch_ones(c(2), dtype=torch_int8)
data <- matrix(1:4, ncol = 2)
tensor$new_tensor(data)
```

## new_zeros

new_zeros(size, dtype=NULL, device=NULL, 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.

#### Arguments:

- 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 NULL, same`torch_dtype`

as this tensor. - device (
`torch_device`

, optional): the desired device of returned tensor. Default: if NULL, same`torch_device`

as this tensor. - requires_grad (bool, optional): If autograd should record operations on the
- returned tensor. Default:
`FALSE`

.

#### Examples:

```
tensor <- torch_tensor(c(1), dtype=torch_float64())
tensor$new_zeros(c(2, 3))
```

## norm

See `?torch_norm`

## normal_

normal_(mean=0, std=1, *, generator=NULL) -> Tensor

Fills `self`

tensor with elements samples from the normal
distribution parameterized by `mean`

and
`std`

.

## numpy

numpy() -> numpy.ndarray

Returns `self`

tensor as a NumPy
:class:`ndarray`

. This tensor and the returned
`ndarray`

share the same underlying storage. Changes to
`self`

tensor will be reflected in the
:class:`ndarray`

and vice versa.

## permute

permute(*dims) -> Tensor

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

#### Examples:

```
x <- torch_randn(2, 3, 5)
x$size()
x$permute(c(3, 1, 2))$size()
```

## put_

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.

#### Arguments:

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

#### Examples:

```
src <- torch_tensor(matrix(3:8, ncol = 3))
src$put_(torch_tensor(1:2), torch_tensor(9:10))
```

## q_per_channel_axis

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.

## q_per_channel_scales

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

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_scale

q_scale() -> float

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

## q_zero_point

q_zero_point() -> int

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

## random_

random_(from=0, to=NULL, *, generator=NULL) -> 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]`

.

## real

Returns a new tensor containing real values of the `self`

tensor. The returned tensor and `self`

share the same
underlying storage.

#### Examples:

```
x <- torch_randn(4, dtype=torch_cfloat())
x
x$real
```

## record_stream

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.

## refine_names

Refines the dimension names of `self`

according to
`names`

.

Refining is a special case of renaming that “lifts” unnamed
dimensions. A `NULL`

dim can be refined to have any name; a
named dim can only be refined to have the same name.

Because named tensors can coexist with unnamed tensors, refining names gives a nice way to write named-tensor-aware code that works with both named and unnamed tensors.

`names`

may contain up to one Ellipsis (`...`

).
The Ellipsis is expanded greedily; it is expanded in-place to fill
`names`

to the same length as `self$dim()`

using
names from the corresponding indices of `self$names`

.

#### Arguments:

- names (iterable of str): The desired names of the output tensor. May contain up to one Ellipsis.

#### Examples:

```
imgs <- torch_randn(32, 3, 128, 128)
named_imgs <- imgs$refine_names(c('N', 'C', 'H', 'W'))
named_imgs$names
```

## register_hook

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 NULL

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(c(0., 0., 0.), requires_grad=TRUE)
h <- v$register_hook(function(grad) grad * 2) # double the gradient
v$backward(torch_tensor(c(1., 2., 3.)))
v$grad
h$remove()
```

## rename

Renames dimension names of `self`

.

There are two main usages:

`self$rename(**rename_map)`

returns a view on tensor that
has dims renamed as specified in the mapping
`rename_map`

.

`self$rename(*names)`

returns a view on tensor, renaming
all dimensions positionally using `names`

. Use
`self$rename(NULL)`

to drop names on a tensor.

One cannot specify both positional args `names`

and
keyword args `rename_map`

.

#### Examples:

```
imgs <- torch_rand(2, 3, 5, 7, names=c('N', 'C', 'H', 'W'))
renamed_imgs <- imgs$rename(c("Batch", "Channels", "Height", "Width"))
```

## repeat

repeat(*sizes) -> Tensor

Repeats this tensor along the specified dimensions.

Unlike `$expand`

, this function copies the tensor’s
data.

#### Arguments:

- sizes (torch_Size or int…): The number of times to repeat this tensor along each
- dimension

#### Examples:

```
x <- torch_tensor(c(1, 2, 3))
x$`repeat`(c(4, 2))
x$`repeat`(c(4, 2, 1))$size()
```

## requires_grad_

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`

.

#### Arguments:

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

.

#### Examples:

```
# Let's say we want to preprocess some saved weights and use
# the result as new weights.
saved_weights <- c(0.1, 0.2, 0.3, 0.25)
loaded_weights <- torch_tensor(saved_weights)
weights <- preprocess(loaded_weights) # some function
weights
# Now, start to record operations done to weights
weights$requires_grad_()
out <- weights$pow(2)$sum()
out$backward()
weights$grad
```

## reshape

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
`$view`

on when it is possible to return a view.

See `?torch_reshape`

## reshape_as

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
`$view`

on when it is possible to return a view.

Please see `reshape`

for more information about
`reshape`

.

## resize_

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_()`

.

#### Arguments:

- 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`

.

#### Examples:

```
x <- torch_tensor(matrix(1:6, ncol = 2))
x$resize_(c(2, 2))
```

## resize_as_

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())`

.

## scatter_

scatter_(dim, index, src) -> 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 have same number of dimensions. It is also required
that `index.size(d) <= src.size(d)`

for all dimensions
`d`

, and that `index.size(d) <= self$size(d)`

for all dimensions `d != dim`

.

Moreover, as for `$gather`

, the values of
`index`

must be between `0`

and
`self$size(dim) - 1`

inclusive, and all values in a row along
the specified dimension `dim`

must be unique.

#### Arguments:

- dim (int): the axis along which to index
- index (LongTensor): the indices of elements to scatter,
- can be either empty or the same size of src. When empty, the operation returns identity
- src (Tensor): the source element(s) to scatter,
- incase
`value`

is not specified - value (float): the source element(s) to scatter,
- incase
`src`

is not specified

#### Examples:

```
x <- torch_rand(2, 5)
x
torch_zeros(3, 5)$scatter_(
1,
torch_tensor(rbind(c(2, 3, 3, 1, 1), c(3, 1, 1, 2, 3)), x)
)
z <- torch_zeros(2, 4)$scatter_(
2,
torch_tensor(matrix(3:4, ncol = 1)), 1.23
)
```

## scatter_add_

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:

In some circumstances when using the CUDA backend with CuDNN, this
operator may select a nondeterministic algorithm to increase
performance. If this is undesirable, you can try to make the operation
deterministic (potentially at a performance cost) by setting
`torch_backends.cudnn.deterministic = TRUE`

.

#### Arguments:

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

#### Examples:

```
x <- torch_rand(2, 5)
x
torch_ones(3, 5)$scatter_add_(1, torch_tensor(rbind(c(0, 1, 2, 0, 0), c(2, 0, 0, 1, 2))), x)
```

## select

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.

## set_

set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -> 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.

## share_memory_

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

short(memory_format=torch_preserve_format) -> Tensor

`self$short()`

is equivalent to
`self$to(torch_int16)`

. See [to()].

## size

size() -> torch_Size

Returns the size of the `self`

tensor. The returned value
is a subclass of `tuple`

.

#### Examples:

`torch_empty(3, 4, 5)$size()`

## sparse_dim

sparse_dim() -> int

If `self`

is a sparse COO tensor (i.e., with
`torch_sparse_coo`

layout), this returns the number of sparse
dimensions. Otherwise, this throws an error.

See also `Tensor.dense_dim`

.

## sparse_mask

sparse_mask(input, mask) -> Tensor

Returns a new SparseTensor with values from Tensor `input`

filtered by indices of `mask`

and values are ignored.
`input`

and `mask`

must have the same shape.

## split

See `?torch_split`

## stft

See `?torch_stft`

## storage_offset

storage_offset() -> int

Returns `self`

tensor’s offset in the underlying storage
in terms of number of storage elements (not bytes).

#### Examples:

```
x <- torch_tensor(c(1, 2, 3, 4, 5))
x$storage_offset()
x[3:N]$storage_offset()
```

## stride

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`

.

#### Examples:

```
x <- torch_tensor(matrix(1:10, nrow = 2))
x$stride()
x$stride(1)
x$stride(-1)
```

## sub

sub(other, *, alpha=1) -> Tensor

Subtracts a scalar or tensor from `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 <broadcasting-semantics>`

with
the shape of the underlying tensor.

## sum_to_size

sum_to_size(*size) -> Tensor

Sum `this`

tensor to `size`

. `size`

must be broadcastable to `this`

tensor size.

## to

to(*args, **kwargs) -> Tensor

Performs Tensor dtype and/or device conversion. A
`torch_dtype`

and :class:`torch_device`

are
inferred from the arguments of
`self$to(*args, **kwargs)`

.

#### Note:

If the `self`

Tensor already has the correct
`torch_dtype`

and :class:`torch_device`

, then
`self`

is returned. Otherwise, the returned tensor is a copy
of `self`

with the desired `torch_dtype`

and
:class:`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`

#### Arguments:

- memory_format (
`torch_memory_format`

, optional): the desired memory format of returned Tensor. Default:`torch_preserve_format`

.

to(device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor

Returns a Tensor with the specified `device`

and
(optional) `dtype`

. If `dtype`

is
`NULL`

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.

#### Arguments:

- memory_format (
`torch_memory_format`

, optional): the desired memory format of returned Tensor. Default:`torch_preserve_format`

.

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

Returns a Tensor with same `torch_dtype`

and
:class:`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.

#### Examples:

```
tensor <- torch_randn(2, 2) # Initially dtype=float32, device=cpu
tensor$to(dtype = torch_float64())
other <- torch_randn(1, dtype=torch_float64())
tensor$to(other = other, non_blocking=TRUE)
```

## to_sparse

to_sparse(sparseDims) -> Tensor Returns a sparse copy of the
tensor. PyTorch supports sparse tensors in
`coordinate format <sparse-docs>`

.

## tolist

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.

## triangular_solve

triangular_solve(A, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -> (Tensor, Tensor)

See [torch_triangular_solve()]

## type

type(dtype=NULL, 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.

#### Arguments:

- 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

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())`

## unfold

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.

## uniform_

uniform_(from=0, to=1) -> Tensor

Fills `self`

tensor with numbers sampled from the
continuous uniform distribution:

\[ P(x) = \dfrac{1}{\text{to} - \text{from}} \]

## unique_consecutive

Eliminates all but the first element from every consecutive group of equivalent elements.

See [torch_unique_consecutive()]

## values

values() -> Tensor

If `self`

is a sparse COO tensor (i.e., with
`torch_sparse_coo`

layout), this returns a view of the
contained values tensor. Otherwise, this throws an error.

## view

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, \dots, d+k`

that satisfy the following
contiguity-like condition that
`\forall i = d, \dots, d+k-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 :meth:`reshape`

,
which returns a view if the shapes are compatible, and copies
(equivalent to calling `contiguous`

) otherwise.

## view_as

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`

.

## where

where(condition, y) -> Tensor

`self$where(condition, y)`

is equivalent to
`torch_where(condition, self, y)`

. See
`?torch_where`