docstr: Factory class to construct a `LinearModel` with sklearn training method.
  
  Please note that this assumes:
  
  0. You have sklearn and numpy installed
  1. The dataset can fit into memory
  
  SkLearn support does introduce some slight overhead as we convert the
  tensors to numpy and then convert the resulting trained model to a
  `LinearModel` object. However, this conversion should be negligible.
  
  Args:
      sklearn_module
          The module under sklearn to construct and use for training, e.g.
          use "svm.LinearSVC" for an SVM or "linear_model.Lasso" for Lasso.
  
          There are factory classes defined for you for common use cases,
          such as `SkLearnLasso`.
      kwargs
          The kwargs to pass to the construction of the sklearn model
code: def __init__(self, sklearn_module: str, **kwargs) -> None:
      r"""
      Factory class to construct a `LinearModel` with sklearn training method.
  
      Please note that this assumes:
  
      0. You have sklearn and numpy installed
      1. The dataset can fit into memory
  
      SkLearn support does introduce some slight overhead as we convert the
      tensors to numpy and then convert the resulting trained model to a
      `LinearModel` object. However, this conversion should be negligible.
  
      Args:
          sklearn_module
              The module under sklearn to construct and use for training, e.g.
              use "svm.LinearSVC" for an SVM or "linear_model.Lasso" for Lasso.
  
              There are factory classes defined for you for common use cases,
              such as `SkLearnLasso`.
          kwargs
              The kwargs to pass to the construction of the sklearn model
      """
      # avoid cycles
      from captum._utils.models.linear_model.train import (
          sklearn_train_linear_model,
      )
  
      super().__init__(train_fn=sklearn_train_linear_model, **kwargs)
  
      self.sklearn_module = sklearn_module
  
imports: from typing import Callable, cast, List, Optional
  import torch.nn
  from captum._utils.models.model import Model
  from torch import Tensor
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new SkLearnLinearModel object from inherited base classes: LinearModel with example: obj = SkLearnLinearModel(sklearn_module)