class_docstr: A hook that contains callbacks to be executed just before and after the forward method of a model. The difference
  with PyTorch existing hooks is that they get passed along the kwargs.
  
  Class attribute:
  - **no_grad** (`bool`, *optional*, defaults to `False`) -- Whether or not to execute the actual forward pass under
    the `torch.no_grad()` context manager.
docstr: To be executed just after the forward method of the model.
  
  Args:
      module (`torch.nn.Module`): The module whose forward pass been executed just before this event.
      output (`Any`): The output of the module.
  
  Returns:
      `Any`: The processed `output`.
docstr_tok: ["To","be","executed","just","after","the","forward","method","of","the","model",".","Args",":","module","(","`","torch.nn.Module","`",")",":","The","module","whose","forward","pass","been","executed","just","before","this","event",".","output","(","`","Any","`",")",":","The","output","of","the","module",".","Returns",":","`","Any","`",":","The","processed","`","output","`","."]
code: def post_forward(self, module, output):
      """
      To be executed just after the forward method of the model.
  
      Args:
          module (`torch.nn.Module`): The module whose forward pass been executed just before this event.
          output (`Any`): The output of the module.
  
      Returns:
          `Any`: The processed `output`.
      """
      return output
  
imports: import functools
  from typing import Dict, List, Mapping, Optional, Union
  import torch
  import torch.nn
  from .state import PartialState
  from .utils import PrefixedDataset, find_device, named_module_tensors, send_to_device, set_module_tensor_to_device
  from .utils.modeling import get_non_persistent_buffers
label_desc: Use image node_id 3 for calling the ModelHook obj's underlying member method code with example usage: obj.post_forward(module, output) and returns: output