code: def __init__(
      self,
      model,
      n_tasks,
      n_feat,
      logdir=None,
      batch_size=50,
      final_loss="weighted_L2",
      learning_rate=0.001,
      optimizer_type="adam",
      learning_rate_decay_time=1000,
      beta1=0.9,
      beta2=0.999,
      pad_batches=True,
      verbose=True,
  ):
      warnings.warn(
          "MultitaskGraphRegressor is deprecated. "
          "Will be removed in DeepChem 1.4.",
          DeprecationWarning,
      )
  
      super(MultitaskGraphRegressor, self).__init__(
          model_dir=logdir, verbose=verbose
      )
      self.n_tasks = n_tasks
      self.final_loss = final_loss
      self.model = model
      self.sess = tf.Session(graph=self.model.graph)
  
      with self.model.graph.as_default():
          # Extract model info
          self.batch_size = batch_size
          self.pad_batches = pad_batches
          # Get graph topology for x
          self.graph_topology = self.model.get_graph_topology()
          self.feat_dim = n_feat
  
          # Building outputs
          self.outputs = self.build()
          self.loss_op = self.add_training_loss(
              self.final_loss, self.outputs
          )
  
          self.learning_rate = learning_rate
          self.T = learning_rate_decay_time
          self.optimizer_type = optimizer_type
  
          self.optimizer_beta1 = beta1
          self.optimizer_beta2 = beta2
  
          # Set epsilon
          self.epsilon = 1e-7
          self.add_optimizer()
  
          # Initialize
          self.init_fn = tf.global_variables_initializer()
          self.sess.run(self.init_fn)
  
          # Path to save checkpoint files, which matches the
          # replicated supervisor's default path.
          self._save_path = os.path.join(self.model_dir, "model.ckpt")
  
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 1 to create a new MultitaskGraphRegressor object from inherited base classes: Model with example: obj = MultitaskGraphRegressor(model, n_tasks, n_feat, logdir, batch_size, final_loss, learning_rate, optimizer_type, learning_rate_decay_time, beta1, beta2, pad_batches, verbose)