class_docstr: Simple class that holds information needed to run Tensorflow graph.
class_docstr_tok: ["Simple","class","that","holds","information","needed","to","run","Tensorflow","graph","."]
code: def __init__(
      self, graph, session, name_scopes, output, labels, weights, loss
  ):
      self.graph = graph
      self.session = session
      self.name_scopes = name_scopes
      self.output = output
      self.labels = labels
      self.weights = weights
      self.loss = loss
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import collections
  import pickle
  import os
  import time
  import warnings
  import numpy
  import pandas
  import tensorflow
  import tempfile
  from deepchem.models import Model
  from deepchem.metrics import from_one_hot
  from deepchem.nn import model_ops
  from deepchem.models.tensorflow_models import utils
  from deepchem.trans import undo_transforms
  from deepchem.utils.save import log
  from deepchem.utils.evaluate import Evaluator
  from deepchem.data import pad_features
  from tensorflow.contrib.layers.python.layers import batch_norm
label_desc: Use image node_id 1 to create a new TensorflowGraph object from inherited base classes: object with example: obj = TensorflowGraph(graph, session, name_scopes, output, labels, weights, loss)