docstr: Returns class probabilities on batch
docstr_tok: ["Returns","class","probabilities","on","batch"]
code: def predict_proba_on_batch(self, X, n_classes=2):
      """Returns class probabilities on batch"""
      # run eval data through the model
      if self.pad_batches:
          X = pad_features(self.batch_size, X)
      n_tasks = self.n_tasks
      with self.sess.as_default():
          feed_dict = self.construct_feed_dict(X)
          batch_outputs = self.sess.run(
              self.outputs, feed_dict=feed_dict
          )
  
      n_samples = len(X)
      outputs = np.zeros((n_samples, self.n_tasks, n_classes))
      for task, output in enumerate(batch_outputs):
          outputs[:, task, :] = output
      return outputs
  
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 12 for calling the MultitaskGraphClassifier obj's underlying member method code with example usage: obj.predict_proba_on_batch(X, n_classes) and returns: outputs