class_docstr: An abstract class to define model data influence skeleton.
docstr: Args:
      model (torch.nn.Module): An instance of pytorch model.
      train_dataset (torch.utils.data.Dataset): PyTorch Dataset that is
              used to create a PyTorch Dataloader to iterate over the dataset and
              its labels. This is the dataset for which we will be seeking for
              influential instances. In most cases this is the training dataset.
      **kwargs: Additional key-value arguments that are necessary for specific
              implementation of `DataInfluence` abstract class.
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code: def __init_(
      self, model: Module, train_dataset: Dataset, **kwargs: Any
  ) -> None:
      r"""
      Args:
          model (torch.nn.Module): An instance of pytorch model.
          train_dataset (torch.utils.data.Dataset): PyTorch Dataset that is
                  used to create a PyTorch Dataloader to iterate over the dataset and
                  its labels. This is the dataset for which we will be seeking for
                  influential instances. In most cases this is the training dataset.
          **kwargs: Additional key-value arguments that are necessary for specific
                  implementation of `DataInfluence` abstract class.
      """
      self.model = model
      self.train_dataset = train_dataset
  
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 1 for calling the DataInfluence obj's underlying member method code with example usage: obj.__init_(model, train_dataset) without return types