code: def add_interim_values(module, input, output):
      
      try:
          del module.x
      except AttributeError:
          pass
      try:
          del module.y
      except AttributeError:
          pass
      module_type = module.__class__.__name__
      if module_type in op_handler:
          func_name = op_handler[module_type].__name__
          # First, check for cases where we don't need to save the x and y tensors
          if func_name == 'passthrough':
              pass
          else:
              # check only the 0th input varies
              for i in range(len(input)):
                  if i != 0 and type(output) is tuple:
                      assert input[i] == output[i], "Only the 0th input may vary!"
              # if a new method is added, it must be added here too. This ensures tensors
              # are only saved if necessary
              if func_name in ['maxpool', 'nonlinear_1d']:
                  # only save tensors if necessary
                  if type(input) is tuple:
                      setattr(module, 'x', torch.nn.Parameter(input[0].detach()))
                  else:
                      setattr(module, 'x', torch.nn.Parameter(input.detach()))
                  if type(output) is tuple:
                      setattr(module, 'y', torch.nn.Parameter(output[0].detach()))
                  else:
                      setattr(module, 'y', torch.nn.Parameter(output.detach()))
              if module_type in failure_case_modules:
                  input[0].register_hook(deeplift_tensor_grad)
docs: The forward hook used to save interim tensors, detached
      from the graph. Used to calculate the multipliers