code: def identity(module, name):
      r"""Apply pruning reparametrization without pruning any units.
  
      Applies pruning reparametrization to the tensor corresponding to the
      parameter called ``name`` in ``module`` without actually pruning any
      units. Modifies module in place (and also return the modified module)
      by:
  
      1) adding a named buffer called ``name+'_mask'`` corresponding to the
         binary mask applied to the parameter ``name`` by the pruning method.
      2) replacing the parameter ``name`` by its pruned version, while the
         original (unpruned) parameter is stored in a new parameter named
         ``name+'_orig'``.
  
      Note:
          The mask is a tensor of ones.
  
      Args:
          module (nn.Module): module containing the tensor to prune.
          name (str): parameter name within ``module`` on which pruning
                  will act.
  
      Returns:
          module (nn.Module): modified (i.e. pruned) version of the input module
  
      Examples:
          >>> # xdoctest: +SKIP
          >>> m = prune.identity(nn.Linear(2, 3), 'bias')
          >>> print(m.bias_mask)
          tensor([1., 1., 1.])
      """
      Identity.apply(module, name)
      return module
  
imports: import numbers
  from abc import ABC, abstractmethod
  from collections.abc import Iterable
  from typing import Tuple
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: identity(module, name) and returns: module