code: def renorm(input, p, dim, maxnorm, *, out=None):
      # Torch hardcodes this magic number
      epsilon = 1e-07
  
      # To iterate through the n-th dimension of `input`, it is easiest to swap
      # the dimension that we wish to iterate through to be first, then iterate
      # through the re-ordered data. This re-ordering is fine for our purposes
      # as we calculate the p-norms and they are all order agnostic. That is,
      # we may re-order the elements of any vector, and as long as none are
      # added, edited, or removed, the p-norm will be the same.
      input_swapped = ivy.swapaxes(input, 0, dim)
      individual_tensors = [
          input_swapped[i, ...] for i in range(input_swapped.shape[0])
      ]
      ret = []
      for individual_tensor in individual_tensors:
          # These tensors may be multidimensional, but must be treated as a single vector.
          original_shape = individual_tensor.shape
          tensor_flattened = ivy.flatten(individual_tensor)
  
          # Don't scale up to the maximum norm, only scale down to it.
          norm = ivy.vector_norm(tensor_flattened, axis=0, ord=p)
          multiplier = ivy.minimum(
              maxnorm / (norm + epsilon), ivy.ones_like(norm)
          )
  
          # Store the result in its original shape
          ret.append(
              ivy.reshape(
                  ivy.multiply(tensor_flattened, multiplier),
                  original_shape,
              )
          )
  
      # We must undo our axis swap from the start.
      ret = ivy.asarray(ret, dtype=ret[0].dtype)
      ret = ivy.swapaxes(ret, 0, dim)
      ret = ivy.reshape(ret, input.shape)
  
      if ivy.exists(out):
          ivy.inplace_update(out, ret)
      return ret
  
imports: import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
  from ivy.functional.frontends.torch import promote_types_of_torch_inputs
  import ivy.functional.frontends.torch
label_desc: Use image node_id 31 for calling a global function with example usage: renorm(input, p, dim, maxnorm) and returns: ret