docstr: To create a simple TensorFlowClassifier for testing.
  :return:
docstr_tok: ["To","create","a","simple","TensorFlowClassifier","for","testing",".",":","return",":"]
code: def _create_tfclassifier():
      """
      To create a simple TensorFlowClassifier for testing.
      :return:
      """
      import tensorflow as tf
  
      # Define input and output placeholders
      input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
      labels_ph = tf.placeholder(tf.int32, shape=[None, 10])
  
      # Define the TensorFlow graph
      conv = tf.layers.conv2d(input_ph, 4, 5, activation=tf.nn.relu)
      conv = tf.layers.max_pooling2d(conv, 2, 2)
      fc = tf.layers.flatten(conv)
  
      # Logits layer
      logits = tf.layers.dense(fc, 10)
  
      # Train operator
      loss = tf.reduce_mean(
          tf.losses.softmax_cross_entropy(
              logits=logits, onehot_labels=labels_ph
          )
      )
      optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
      train = optimizer.minimize(loss)
  
      # TensorFlow session and initialization
      sess = tf.Session()
      sess.run(tf.global_variables_initializer())
  
      # Create the classifier
      tfc = TensorFlowClassifier(
          input_ph=input_ph,
          output=logits,
          labels_ph=labels_ph,
          train=train,
          loss=loss,
          learning=None,
          sess=sess,
          clip_values=(0, 1),
      )
  
      return tfc
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import numpy
  import tensorflow
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from art.attacks.evasion.hop_skip_jump import HopSkipJump
  from art.estimators.classification.keras import KerasClassifier
  from art.estimators.classification.pytorch import PyTorchClassifier
  from art.estimators.classification.tensorflow import TensorFlowClassifier
  from art.metrics.metrics import adversarial_accuracy, empirical_robustness, clever_t, clever_u, clever, loss_sensitivity, wasserstein_distance
  from art.utils import load_mnist
  from tests.utils import master_seed
label_desc: Use image node_id 6 for calling the TestMetrics obj's underlying member method code with example usage: obj._create_tfclassifier() and returns: tfc