class_docstr: "
  This class implement the second order optimizer - KFAC, which uses Kronecker factor products of inputs and the gradients to
  get the approximate inverse fisher matrix, which is used to update the model parameters. Presently this optimizer works only
  on liner and 2D convolution layers. If you want to know more details about KFAC, please check the paper [1]_ and [2]_.
  
  References:
  -----------
  [1] Martens, James, and Roger Grosse. Optimizing Neural Networks with Kronecker-Factored Approximate Curvature.
  arXiv:1503.05671, arXiv, 7 June 2020. arXiv.org, http://arxiv.org/abs/1503.05671.
  [2] Grosse, Roger, and James Martens. A Kronecker-Factored Approximate Fisher Matrix for Convolution Layers.
  arXiv:1602.01407, arXiv, 23 May 2016. arXiv.org, http://arxiv.org/abs/1602.01407.
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docstr: "
  Attaches hooks(saving the ouptut and grad according to the update function) to the model for
  to calculate gradients at every step.
docstr_tok: ["``","Attaches","hooks","(","saving","the","ouptut","and","grad","according","to","the","update","function",")","to","the","model","for","to","calculate","gradients","at","every","step","."]
code: def _prepare_model(self):
      """ "
      Attaches hooks(saving the ouptut and grad according to the update function) to the model for
      to calculate gradients at every step.
      """
      count = 0
      for module in self.model.modules():
          classname = module.__class__.__name__
          if classname in self.known_modules:
              self.modules.append(module)
              module.register_forward_pre_hook(self._save_input)
              module.register_backward_hook(self._save_grad_output)
              count += 1
  
file_path: deepchem/deepchem/models/torch_models/kfac_optimizer.py
imports: import math
  from typing import Optional, Callable, Dict, Tuple, List, Union
label_desc: Use image node_id 8 for calling the KFACOptimizer obj's underlying member method code with example usage: obj._prepare_model() without return types