torch_randint_like

(Tensor) the size of <code>input</code> will determine size of the output tensor.

input

(int, optional) Lowest integer to be drawn from the distribution. Default: 0.

(int) One above the highest integer to be drawn from the distribution.

high

(<code>torch.dtype</code>, optional) the desired data type of returned Tensor. Default: if <code>None</code>, defaults to the dtype of <code>input</code>.

dtype

(<code>torch.layout</code>, optional) the desired layout of returned tensor. Default: if <code>None</code>, defaults to the layout of <code>input</code>.

layout

(<code>torch.device</code>, optional) the desired device of returned tensor. Default: if <code>None</code>, defaults to the device of <code>input</code>.

device

(bool, optional) If autograd should record operations on the returned tensor. Default: <code>False</code>.

requires_grad

(<code>torch.memory_format</code>, optional) the desired memory format of returned Tensor. Default: <code>torch_preserve_format</code>.

memory_format

Provides functionality to define and train neural networks similar to
'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R
using the 'libtorch' library. Also supports low-level tensor operations and
'GPU' acceleration.

Daniel Falbel

torch

Tensors and Neural Networks with 'GPU' Acceleration

Javier Luraschi

Dmitriy Selivanov

Athos Damiani

 RStudio

torch_randint_like function

memory_format=torch.preserve_format) -&gt; Tensor
Returns a tensor with the same shape as Tensor <code>input</code> filled with
random integers generated uniformly between <code>low</code> (inclusive) and
<code>high</code> (exclusive).
.. note:
With the global dtype default (<code>torch_float32</code>), this function returns
a tensor with dtype <code>torch_int64</code>.

torch_randint_like: Randint_like

Description

Arguments

randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False,