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# 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.]])
      >>> torch.renorm(x, 1, 0, 5)
      tensor([[ 1.0000,  1.0000,  1.0000],
              [ 1.6667,  1.6667,  1.6667],
              [ 1.6667,  1.6667,  1.6667]])

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