class_docstr: Implements a progressive multitask neural network for regression.
  
  Progressive networks allow for multitask learning where each task
  gets a new column of weights. As a result, there is no exponential
  forgetting where previous tasks are ignored.
  
  References
  ----------
  See [1]_ for a full description of the progressive architecture
  
  .. [1] Rusu, Andrei A., et al. "Progressive neural networks." arXiv preprint
      arXiv:1606.04671 (2016).
docstr: Fit one task.
docstr_tok: ["Fit","one","task","."]
code: def fit_task(
      self,
      dataset,
      task,
      nb_epoch=10,
      max_checkpoints_to_keep=5,
      checkpoint_interval=1000,
      deterministic=False,
      restore=False,
      **kwargs,
  ):
      """Fit one task."""
      shape = dataset.get_shape()
      batch = [[np.zeros((self.batch_size,) + s[1:])] for s in shape]
      self._create_training_ops(batch)
      generator = self.default_generator(
          dataset, epochs=nb_epoch, deterministic=deterministic
      )
      variables = []
      for layer in self._task_layers[task]:
          variables += layer.trainable_variables
      loss = TaskLoss(self.model, self.create_loss(), task)
      self.fit_generator(
          generator,
          max_checkpoints_to_keep,
          checkpoint_interval,
          restore,
          variables=variables,
          loss=loss,
      )
  
imports: import numpy
  import tensorflow
  from collections.abc import Sequence
  import logging
  from deepchem.models import KerasModel, layers
  from deepchem.models.losses import L2Loss, SparseSoftmaxCrossEntropy
  from deepchem.models.keras_model import _StandardLoss
  from tensorflow.keras.layers import Input, Dense, Dropout, ReLU, Concatenate, Add, Multiply, Softmax
label_desc: Use image node_id 6 for calling the ProgressiveMultitaskRegressor obj's underlying member method code with example usage: obj.fit_task(dataset, task, nb_epoch, max_checkpoints_to_keep, checkpoint_interval, deterministic, restore) without return types