code: def deeplift_grad(module, grad_input, grad_output):
      """The backward hook which computes the deeplift
      gradient for an nn.Module
      """
      # first, get the module type
      module_type = module.__class__.__name__
      # first, check the module is supported
      if module_type in op_handler:
          if op_handler[module_type].__name__ not in [
              "passthrough",
              "linear_1d",
          ]:
              return op_handler[module_type](
                  module, grad_input, grad_output
              )
      else:
          warnings.warn(f"unrecognized nn.Module: {module_type}")
          return grad_input
  
file_path: shap/shap/explainers/_deep/deep_pytorch.py
imports: import warnings
  import numpy
  from packaging import version
  from .._explainer import Explainer
  from .deep_utils import _check_additivity
label_desc: Use image node_id 1 for calling a global function with example usage: deeplift_grad(module, grad_input, grad_output) and returns: grad_input, op_handler