class_docstr: Abstract base class for DeepChem models.
class_docstr_tok: ["Abstract","base","class","for","DeepChem","models","."]
docstr: Fits a model on data in a Dataset object.
  
  Parameters
  ----------
  dataset: Dataset
      the Dataset to train on
docstr_tok: ["Fits","a","model","on","data","in","a","Dataset","object",".","Parameters","--","--","--","--","--","dataset",":","Dataset","the","Dataset","to","train","on"]
code: def fit(self, dataset: Dataset):
      """
      Fits a model on data in a Dataset object.
  
      Parameters
      ----------
      dataset: Dataset
          the Dataset to train on
      """
      raise NotImplementedError(
          "Each model is responsible for its own fit method."
      )
  
imports: import os
  import shutil
  import tempfile
  import logging
  from typing import List, Optional, Sequence
  import numpy
  from deepchem.data import Dataset
  from deepchem.metrics import Metric
  from deepchem.trans import Transformer, undo_transforms
  from deepchem.utils.evaluate import Evaluator
  from deepchem.utils.typing import OneOrMany
label_desc: Use image node_id 9 for calling the Model obj's underlying member method code with example usage: obj.fit(dataset) without return types