class_docstr: An abstract class to define model data influence skeleton.
docstr: Args:
      inputs (Any): Batch of examples for which influential
              instances are computed. They are passed to the forward_func. If
              `inputs` if a tensor or tuple of tensors, the first dimension
              of a tensor corresponds to the batch dimension.
      **kwargs: Additional key-value arguments that are necessary for specific
              implementation of `DataInfluence` abstract class.
  
  Returns:
      influences (Any): We do not add restrictions on the return type for now,
              though this may change in the future.
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code: def influence(self, inputs: Any = None, **kwargs: Any) -> Any:
      r"""
      Args:
          inputs (Any): Batch of examples for which influential
                  instances are computed. They are passed to the forward_func. If
                  `inputs` if a tensor or tuple of tensors, the first dimension
                  of a tensor corresponds to the batch dimension.
          **kwargs: Additional key-value arguments that are necessary for specific
                  implementation of `DataInfluence` abstract class.
  
      Returns:
          influences (Any): We do not add restrictions on the return type for now,
                  though this may change in the future.
      """
      pass
  
file_path: captum/captum/influence/_core/influence.py
imports: from abc import ABC, abstractmethod
  from typing import Any, Type
  from torch.nn import Module
  from torch.utils.data import Dataset
label_desc: Use image node_id 2 for calling the DataInfluence obj's underlying member method code with example usage: obj.influence(inputs) without return types