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# torch.randn

torch.randn(*size, *, generator=None, 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).

  out i∼N(0,1)\text{out}_{i} \sim \mathcal{N}(0, 1) out i​∼N(0,1)

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

  Parameters:

    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 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: if `None`, uses a global default (see `torch.set_default_tensor_type()`).

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

    - device (`torch.device`, optional) – the desired device of returned tensor. Default: if `None`, uses the current device for the default tensor type (see `torch.set_default_tensor_type()`). `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]])

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