class_docstr: A base class for training hooks in PyTorch.
  
  This class provides a template for implementing custom training hooks
  that can be activated, deactivated, and maintain a handle to the hook.
  
  Attributes:
      _hook_handle (Optional[torch.utils.hooks.RemovableHandle]): A handle to the
          registered forward hook, initially set to None.
docstr: Activates the training hook for a given module.
  
  Args:
      module (nn.Module): The PyTorch module for which the hook is activated.
  
  Returns:
      nn.Module: The input module with the training hook activated.
docstr_tok: ["Activates","the","training","hook","for","a","given","module",".","Args",":","module","(","nn.Module",")",":","The","PyTorch","module","for","which","the","hook","is","activated",".","Returns",":","nn.Module",":","The","input","module","with","the","training","hook","activated","."]
code: def activate_hook(self, module: torch.nn.Module) -> torch.nn.Module:
      """Activates the training hook for a given module.
  
      Args:
          module (nn.Module): The PyTorch module for which the hook is activated.
  
      Returns:
          nn.Module: The input module with the training hook activated.
      """
      self._hook_handle = module.register_forward_hook(self.hook_fn)
      return module
  
file_path: ludwig/ludwig/modules/training_hooks.py
imports: import logging
  from abc import ABC, abstractmethod
  import torch
label_desc: Use image node_id 3 for calling the TrainingHook obj's underlying member method code with example usage: obj.activate_hook(module) and returns: module