torch.renorm
torch.renorm(input, p, dim, maxnorm, *, out=None) -> Tensor
Returns a tensor where each sub-tensor of "input" along dimension
   "dim" is normalized such that the p-norm of the sub-tensor is
   lower than the value "maxnorm"
Note:
 If the norm of a row is lower than *maxnorm*, the row is
 unchanged

Parameters:
      * input (Tensor) -- the input tensor.
  * **p** (*float*) -- the power for the norm computation

  * **dim** (*int*) -- the dimension to slice over to get the sub-
    tensors

  * **maxnorm** (*float*) -- the maximum norm to keep each sub-
    tensor under

Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example:
  >>> x = torch.ones(3, 3)
  >>> x[1].fill_(2)
  tensor([ 2.,  2.,  2.])
  >>> x[2].fill_(3)
  tensor([ 3.,  3.,  3.])
  >>> x
  tensor([[ 1.,  1.,  1.],
          [ 2.,  2.,  2.],
          [ 3.,  3.,  3.]])
