torch.randint#

torch.randint(low=0,high,size,\*,generator=None,out=None,dtype=None,layout=torch.strided,device=None,requires_grad=False)→Tensor#

Returns a tensor filled with random integers generated uniformlybetweenlow (inclusive) andhigh (exclusive).

The shape of the tensor is defined by the variable argumentsize.

Note

With the global dtype default (torch.float32), this function returnsa tensor with dtypetorch.int64.

Parameters

• 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.

• size (tuple) – a tuple defining the shape of the output tensor.

Keyword Arguments

• generator (torch.Generator, optional) – a pseudorandom number generator for sampling

• out (Tensor,optional) – the output tensor.

• dtype (torch.dtype,optional) – the desired data type of returned tensor. Default: ifNone,this function returns a tensor with dtypetorch.int64.

• layout (torch.layout, optional) – the desired layout of returned Tensor.Default:torch.strided.

• device (torch.device, optional) – the desired device of returned tensor.Default: ifNone, uses the current device for the default tensor type(seetorch.set_default_device()).device will be the CPUfor CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool,optional) – If autograd should record operations on thereturned tensor. Default:False.

Example:

>>>torch.randint(3,5,(3,))tensor([4, 3, 4])>>>torch.randint(10,(2,2))tensor([[0, 2],        [5, 5]])>>>torch.randint(3,10,(2,2))tensor([[4, 5],        [6, 7]])