code: def fit(
      self,
      dataset,
      nb_epoch=10,
      max_checkpoints_to_keep=5,
      log_every_N_batches=50,
      checkpoint_interval=10,
      **kwargs,
  ):
      # Perform the optimization
      log("Training for %d epochs" % nb_epoch, self.verbose)
  
      # TODO(rbharath): Disabling saving for now to try to debug.
      for epoch in range(nb_epoch):
          log("Starting epoch %d" % epoch, self.verbose)
          for batch_num, (X_b, y_b, w_b, ids_b) in enumerate(
              dataset.iterbatches(
                  self.batch_size, pad_batches=self.pad_batches
              )
          ):
              if batch_num % log_every_N_batches == 0:
                  log("On batch %d" % batch_num, self.verbose)
              self.sess.run(
                  self.train_op,
                  feed_dict=self.construct_feed_dict(X_b, y_b, w_b),
              )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import warnings
  import os
  import sys
  import numpy
  import tensorflow
  import sklearn.metrics
  import tempfile
  from deepchem.utils.save import log
  from deepchem.models import Model
  from deepchem.nn.copy import Input
  from deepchem.nn.copy import Dense
  from deepchem.data import pad_features
  from deepchem.nn import model_ops
  from deepchem.models.tf_new_models.graph_topology import merge_dicts
  from deepchem.models.tf_new_models.multitask_classifier import get_loss_fn
label_desc: Use image node_id 6 for calling the MultitaskGraphRegressor obj's underlying member method code with example usage: obj.fit(dataset, nb_epoch, max_checkpoints_to_keep, log_every_N_batches, checkpoint_interval) without return types