code: def random_structured(module, name, amount, dim):
      r"""Prune tensor by removing random channels along the specified dimension.
  
      Prunes tensor corresponding to parameter called ``name`` in ``module``
      by removing the specified ``amount`` of (currently unpruned) channels
      along the specified ``dim`` selected at random.
      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'``.
  
      Args:
          module (nn.Module): module containing the tensor to prune
          name (str): parameter name within ``module`` on which pruning
                  will act.
          amount (int or float): quantity of parameters to prune.
              If ``float``, should be between 0.0 and 1.0 and represent the
              fraction of parameters to prune. If ``int``, it represents the
              absolute number of parameters to prune.
          dim (int): index of the dim along which we define channels to prune.
  
      Returns:
          module (nn.Module): modified (i.e. pruned) version of the input module
  
      Examples:
          >>> # xdoctest: +SKIP
          >>> m = prune.random_structured(
          ...     nn.Linear(5, 3), 'weight', amount=3, dim=1
          ... )
          >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0))
          >>> print(columns_pruned)
          3
      """
      RandomStructured.apply(module, name, amount, dim)
      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 4 for calling a global function with example usage: random_structured(module, name, amount, dim) and returns: module