class_docstr: Implementation of a Basic Iterative Pruner.
  Any iterative pruning technique can be extended from this by implementing the `prune_model` method.
  
  :param model: Model that needs to be pruned
  :type model: torch.nn.Module
  :param train_loader: Dataloader for training
  :type train_loader: torch.utils.data.DataLoader
  :param test_loader: Dataloader for validation/testing
  :type test_loader: torch.utils.data.DataLoader
  :param loss_fn: Loss function to be used for training
  :type loss_fn: torch.nn.Module
  :param device: Device used for implementation ("cpu" by default)
  :type device: torch.device
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docstr: Function used to implement the pruning technique.
  Needs to zero parameters of the model that are pruned by the technique.
docstr_tok: ["Function","used","to","implement","the","pruning","technique",".","Needs","to","zero","parameters","of","the","model","that","are","pruned","by","the","technique","."]
code: def prune_model(self, **prune_params):
      """
      Function used to implement the pruning technique.
      Needs to zero parameters of the model that are pruned by the technique.
      """
  
      raise NotImplementedError
  
file_path: KD_Lib/KD_Lib/Pruning/common/iterative_base_class.py
imports: import copy
  import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 7 for calling the BaseIterativePruner obj's underlying member method code with example usage: obj.prune_model() without return types