torch.nn.functional.normalize
torch.nn.functional.normalize(input, p=2.0, dim=1, eps=1e-12, out=None)
Performs L_p normalization of inputs over specified dimension.
For a tensor "input" of sizes (n_0, ..., n_{dim}, ..., n_k), each
   n_{dim} -element vector v along dimension "dim" is transformed as
  v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)}.

With the default arguments it uses the Euclidean norm over vectors
   along dimension 1 for normalization.
Parameters:
      * input (Tensor) -- input tensor of any shape
  * **p** (*float*) -- the exponent value in the norm formulation.
    Default: 2

  * **dim** (*int*) -- the dimension to reduce. Default: 1

  * **eps** (*float*) -- small value to avoid division by zero.
    Default: 1e-12

  * **out** (*Tensor**, **optional*) -- the output tensor. If
    "out" is used, this operation won't be differentiable.

Return type:
      Tensor