docstr: Get initial information about task normalization
docstr_tok: ["Get","initial","information","about","task","normalization"]
code: def construct_feed_dict(self, X_b, y_b=None, w_b=None, training=True):
      """Get initial information about task normalization"""
      # TODO(rbharath): I believe this is total amount of data
      n_samples = len(X_b)
      if y_b is None:
          y_b = np.zeros((n_samples, self.n_tasks))
      if w_b is None:
          w_b = np.zeros((n_samples, self.n_tasks))
      targets_dict = {
          self.label_placeholder: y_b,
          self.weight_placeholder: w_b,
      }
  
      # Get graph information
      atoms_dict = self.graph_topology.batch_to_feed_dict(X_b)
  
      # TODO (hraut->rhbarath): num_datapoints should be a vector, with ith element being
      # the number of labeled data points in target_i. This is to normalize each task
      # num_dat_dict = {self.num_datapoints_placeholder : self.}
  
      # Get other optimizer information
      # TODO(rbharath): Figure out how to handle phase appropriately
      feed_dict = merge_dicts([targets_dict, atoms_dict])
      return feed_dict
  
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.data import pad_features
  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.nn import model_ops
  from deepchem.models.tf_new_models.graph_topology import merge_dicts
label_desc: Use image node_id 4 for calling the MultitaskGraphClassifier obj's underlying member method code with example usage: obj.construct_feed_dict(X_b, y_b, w_b, training) and returns: feed_dict