code: def norm(self, p="fro", dim=None, keepdim=False):
      """Computes the p-norm of the input tensor (or along a dimension)."""
      if p == "fro":
          p = 2
  
      if isinstance(p, (int, float)):
          assert p >= 1, "p-norm requires p >= 1"
          if p == 1:
              if dim is None:
                  return self.abs().sum()
              return self.abs().sum(dim, keepdim=keepdim)
          elif p == 2:
              if dim is None:
                  return self.square().sum().sqrt()
              return self.square().sum(dim, keepdim=keepdim).sqrt()
          elif p == float("inf"):
              if dim is None:
                  return self.abs().max()
              return self.abs().max(dim=dim, keepdim=keepdim)[0]
          else:
              if dim is None:
                  return self.abs().pos_pow(p).sum().pos_pow(1 / p)
              return (
                  self.abs()
                  .pos_pow(p)
                  .sum(dim, keepdim=keepdim)
                  .pos_pow(1 / p)
              )
      elif p == "nuc":
          raise NotImplementedError("Nuclear norm is not implemented")
      else:
          raise ValueError(f"Improper value p ({p})for p-norm")
  
imports: import crypten
  import torch
  from ..tensor_types import is_tensor
label_desc: Use image node_id 4 for calling a global function with example usage: norm(p, dim, keepdim) and returns: self, self, self, self, self, self, self, self