Randint_like

Usage

torch_randint_like(
  input,
  low,
  high,
  dtype = NULL,
  layout = NULL,
  device = NULL,
  requires_grad = FALSE
)

Arguments

input: (Tensor) the size of input will determine size of the output tensor.
low: (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
high: (int) One above the highest integer to be drawn from the distribution.
dtype: (torch.dtype, optional) the desired data type of returned Tensor. Default: if NULL, defaults to the dtype of input.
layout: (torch.layout, optional) the desired layout of returned tensor. Default: if NULL, defaults to the layout of input.
device: (torch.device, optional) the desired device of returned tensor. Default: if NULL, defaults to the device of input.
requires_grad: (bool, optional) If autograd should record operations on the returned tensor. Default: FALSE.

randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False,

memory_format=torch.preserve_format) -> Tensor

Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive).

.. note: With the global dtype default (torch_float32), this function returns a tensor with dtype torch_int64.