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]])

`