torch.randn(*tensor_shape, **kwargs)

`
randn(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor

Returns a tensor filled with random numbers from a normal distribution
with mean 0 and variance 1 (also called the standard normal
distribution).

.. math::
\text{out}_{i} \sim \mathcal{N}(0, 1)

The shape of the tensor is defined by the variable argument :attr:size.

Args:
size (int...): a sequence of integers defining the shape of the output tensor.
Can be a variable number of arguments or a collection like a list or tuple.

Keyword args:
generator (:class:torch.Generator, optional): a pseudorandom number generator for sampling
out (Tensor, optional): the output tensor.
dtype (:class:torch.dtype, optional): the desired data type of returned tensor.
Default: if None, uses a global default (see :func:torch.set_default_tensor_type).
layout (:class:torch.layout, optional): the desired layout of returned Tensor.
Default: torch.strided.
device (:class:torch.device, optional): the desired device of returned tensor.
Default: if None, uses the current device for the default tensor type
(see :func:torch.set_default_tensor_type). :attr:device will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default: False.
pin_memory (bool, optional): If set, returned tensor would be allocated in
the pinned memory. Works only for CPU tensors. Default: False.

Example::

>>> torch.randn(4)
tensor([-2.1436, 0.9966, 2.3426, -0.6366])
>>> torch.randn(2, 3)
tensor([[ 1.5954, 2.8929, -1.0923],
[ 1.1719, -0.4709, -0.1996]])
`