class_docstr: Abstract base class for DeepChem models.
class_docstr_tok: ["Abstract","base","class","for","DeepChem","models","."]
docstr: Perform a single step of training.
  
  Parameters
  ----------
  X: np.ndarray
      the inputs for the batch
  y: np.ndarray
      the labels for the batch
  w: np.ndarray
      the weights for the batch
docstr_tok: ["Perform","a","single","step","of","training",".","Parameters","--","--","--","--","--","X",":","np.ndarray","the","inputs","for","the","batch","y",":","np.ndarray","the","labels","for","the","batch","w",":","np.ndarray","the","weights","for","the","batch"]
code: def fit_on_batch(self, X: Sequence, y: Sequence, w: Sequence):
      """Perform a single step of training.
  
      Parameters
      ----------
      X: np.ndarray
          the inputs for the batch
      y: np.ndarray
          the labels for the batch
      w: np.ndarray
          the weights for the batch
      """
      raise NotImplementedError(
          "Each model is responsible for its own fit_on_batch 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 3 for calling the Model obj's underlying member method code with example usage: obj.fit_on_batch(X, y, w) without return types