class_docstr: Implements a neural network for robust multitasking.
  
  The key idea of this model is to have bypass layers that feed
  directly from features to task output. This might provide some
  flexibility to route around challenges in multitasking with
  destructive interference.
  
  References
  ----------
  .. [1]   Ramsundar, Bharath, et al. "Is multitask deep learning practical for pharma?." Journal of chemical information and modeling 57.8 (2017): 2068-2076.
class_docstr_tok: ["Implements","a","neural","network","for","robust","multitasking",".","The","key","idea","of","this","model","is","to","have","bypass","layers","that","feed","directly","from","features","to","task","output",".","This","might","provide","some","flexibility","to","route","around","challenges","in","multitasking","with","destructive","interference",".","References","--","--","--","--","--","..","[","1","]","Ramsundar",",","Bharath",",","et","al",".","``","Is","multitask","deep","learning","practical","for","pharma","?",".","''","Journal","of","chemical","information","and","modeling","57.8","(","2017",")",":","2068-2076","."]
code: def default_generator(
      self,
      dataset: dc.data.Dataset,
      epochs: int = 1,
      mode: str = "fit",
      deterministic: bool = True,
      pad_batches: bool = True,
  ) -> Iterable[Tuple[List, List, List]]:
      for epoch in range(epochs):
          for X_b, y_b, w_b, ids_b in dataset.iterbatches(
              batch_size=self.batch_size,
              deterministic=deterministic,
              pad_batches=pad_batches,
          ):
              yield ([X_b], [y_b], [w_b])
  
file_path: deepchem/deepchem/models/robust_multitask.py
imports: import tensorflow
  from collections.abc import Sequence
  import logging
  import deepchem
  from deepchem.metrics import to_one_hot
  from deepchem.models import KerasModel
  from deepchem.models.layers import Stack
  from deepchem.models.losses import SoftmaxCrossEntropy, L2Loss
  from typing import Tuple, Iterable, List
label_desc: Use image node_id 2 for calling the RobustMultitaskRegressor obj's underlying member method code with example usage: obj.default_generator(dataset, epochs, mode, deterministic, pad_batches) without return types