docstr: Make predictions on batch of data.
docstr_tok: ["Make","predictions","on","batch","of","data","."]
code: def predict_proba_on_batch(self, support, test_batch):
      """Make predictions on batch of data."""
      n_samples = len(test_batch)
      X, y, w, ids = pad_batch(
          self.test_batch_size,
          test_batch.X,
          test_batch.y,
          test_batch.w,
          test_batch.ids,
      )
      padded_test_batch = NumpyDataset(X, y, w, ids)
      feed_dict = self.construct_feed_dict(padded_test_batch, support)
      # Get scores
      pred, scores = self.sess.run(
          [self.pred_op, self.scores_op], feed_dict=feed_dict
      )
      # pred corresponds to prob(example == 1)
      y_pred_batch = np.zeros((n_samples, 2))
      # Remove padded elements
      pred = pred[:n_samples]
      y_pred_batch[:, 1] = pred
      y_pred_batch[:, 0] = 1 - pred
      return y_pred_batch
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import warnings
  import numpy
  import tensorflow
  import sys
  import time
  from deepchem.models import Model
  from deepchem.data import pad_batch
  from deepchem.data import NumpyDataset
  from deepchem.metrics import to_one_hot
  from deepchem.metrics import from_one_hot
  from deepchem.models.tf_new_models.graph_topology import merge_dicts
  from deepchem.nn import model_ops
  from deepchem.data import SupportGenerator
  from deepchem.data import EpisodeGenerator
  from deepchem.data import get_task_dataset
  from deepchem.data import get_single_task_test
  from deepchem.data import get_task_dataset_minus_support
  from deepchem.nn.copy import Input
label_desc: Use image node_id 12 for calling the SupportGraphClassifier obj's underlying member method code with example usage: obj.predict_proba_on_batch(support, test_batch) and returns: y_pred_batch