docstr: To create a simple PyTorchClassifier for testing.
  :return:
docstr_tok: ["To","create","a","simple","PyTorchClassifier","for","testing",".",":","return",":"]
code: def _create_ptclassifier():
      """
      To create a simple PyTorchClassifier for testing.
      :return:
      """
  
      class Model(nn.Module):
          def __init__(self):
              super(Model, self).__init__()
              self.conv = nn.Conv2d(1, 16, 5)
              self.pool = nn.MaxPool2d(2, 2)
              self.fc = nn.Linear(2304, 10)
  
          def forward(self, x):
              x = self.pool(f.relu(self.conv(x)))
              x = x.view(-1, 2304)
              logit_output = self.fc(x)
  
              return logit_output
  
      # Define the network
      model = Model()
  
      # Define a loss function and optimizer
      loss_fn = nn.CrossEntropyLoss()
      optimizer = optim.Adam(model.parameters(), lr=0.01)
  
      # Get classifier
      ptc = PyTorchClassifier(
          model=model,
          loss=loss_fn,
          optimizer=optimizer,
          input_shape=(1, 28, 28),
          nb_classes=10,
          clip_values=(0, 1),
      )
  
      return ptc
  
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 8 for calling the TestMetrics obj's underlying member method code with example usage: obj._create_ptclassifier() and returns: ptc, logit_output