class_docstr: Extends DeepLift algorithm and approximates SHAP values using Deeplift.
  For each input sample it computes DeepLift attribution with respect to
  each baseline and averages resulting attributions.
  More details about the algorithm can be found here:
  
  http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf
  
  Note that the explanation model:
      1. Assumes that input features are independent of one another
      2. Is linear, meaning that the explanations are modeled through
          the additive composition of feature effects.
  Although, it assumes a linear model for each explanation, the overall
  model across multiple explanations can be complex and non-linear.
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docstr: Args:
  
      model (nn.Module):  The reference to PyTorch model instance.
docstr_tok: ["Args",":","model","(","nn.Module",")",":","The","reference","to","PyTorch","model","instance","."]
code: def __init__(self, model: Module) -> None:
      r"""
      Args:
  
          model (nn.Module):  The reference to PyTorch model instance.
      """
      DeepLift.__init__(self, model)
  
imports: import typing
  import warnings
  from typing import Any, Callable, List, Tuple, Union, cast
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.hooks import RemovableHandle
  from captum.attr._utils.attribution import GradientAttribution
  from captum.attr._utils.common import ExpansionTypes, _call_custom_attribution_func, _compute_conv_delta_and_format_attrs, _expand_additional_forward_args, _expand_target, _format_additional_forward_args, _format_attributions, _format_baseline, _format_callable_baseline, _format_input, _format_tensor_into_tuples, _is_tuple, _run_forward, _tensorize_baseline, _validate_input
  from captum.attr._utils.gradient import apply_gradient_requirements, undo_gradient_requirements
  from captum.attr._utils.typing import BaselineType, Literal, TargetType, TensorOrTupleOfTensorsGeneric
label_desc: Use image node_id 1 to create a new DeepLiftShap object from inherited base classes: DeepLift with example: obj = DeepLiftShap(model)