docstr: Builds a support-based classifier.
  
  See https://arxiv.org/pdf/1606.04080v1.pdf for definition of support.
  
  Parameters
  ----------
  sess: tf.Session
    Session for this model
  model: SequentialSupportModel 
    Contains core layers in model. 
  n_pos: int
    Number of positive examples in support.
  n_neg: int
    Number of negative examples in support.
docstr_tok: ["Builds","a","support-based","classifier",".","See","https",":","\/\/arxiv.org\/pdf\/1606.04080v1.pdf","for","definition","of","support",".","Parameters","--","--","--","--","--","sess",":","tf.Session","Session","for","this","model","model",":","SequentialSupportModel","Contains","core","layers","in","model",".","n_pos",":","int","Number","of","positive","examples","in","support",".","n_neg",":","int","Number","of","negative","examples","in","support","."]
code: def __init__(
      self,
      model,
      test_batch_size=10,
      support_batch_size=10,
      learning_rate=0.001,
      similarity="cosine",
      **kwargs,
  ):
      """Builds a support-based classifier.
  
      See https://arxiv.org/pdf/1606.04080v1.pdf for definition of support.
  
      Parameters
      ----------
      sess: tf.Session
        Session for this model
      model: SequentialSupportModel
        Contains core layers in model.
      n_pos: int
        Number of positive examples in support.
      n_neg: int
        Number of negative examples in support.
      """
      warnings.warn(
          "SupportGraphClassifier is deprecated. "
          "Will be removed in DeepChem 1.4.",
          DeprecationWarning,
      )
      self.similarity = similarity
      self.model = model
      self.sess = tf.Session(graph=self.model.graph)
      self.test_batch_size = test_batch_size
      self.support_batch_size = support_batch_size
  
      self.learning_rate = learning_rate
      self.epsilon = 1e-7
  
      with self.model.graph.as_default():
          self.add_placeholders()
          (
              self.pred_op,
              self.scores_op,
              self.loss_op,
          ) = self.add_training_loss()
          # Get train function
          self.train_op = self.get_training_op(self.loss_op)
  
          # Initialize
          self.init_fn = tf.global_variables_initializer()
          self.sess.run(self.init_fn)
  
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 1 to create a new SupportGraphClassifier object from inherited base classes: Model with example: obj = SupportGraphClassifier(model, test_batch_size, support_batch_size, learning_rate, similarity)