class_docstr: Parent class for deepchem Tensorflow models.
  
  Classifier:
    n_classes
  
  Has the following attributes:
  
    placeholder_root: String placeholder prefix, used to create
      placeholder_scope.
  
  Generic base class for defining, training, and evaluating TensorflowGraphs.
  
  Subclasses must implement the following methods:
    build
    add_output_ops
    add_training_cost 
  
  Args:
    train: If True, model is in training mode.
    logdir: Directory for output files.
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docstr: Transform a minibatch of data into a feed_dict.
  
  Raises:
    NotImplementedError: if not overridden by concrete subclass.
docstr_tok: ["Transform","a","minibatch","of","data","into","a","feed_dict",".","Raises",":","NotImplementedError",":","if","not","overridden","by","concrete","subclass","."]
code: def construct_feed_dict(self, X_b, y_b=None, w_b=None, ids_b=None):
      """Transform a minibatch of data into a feed_dict.
  
      Raises:
        NotImplementedError: if not overridden by concrete subclass.
      """
      raise NotImplementedError(
          "Must be overridden by concrete subclass"
      )
  
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 10 for calling the TensorflowGraphModel obj's underlying member method code with example usage: obj.construct_feed_dict(X_b, y_b, w_b, ids_b) without return types