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

        To create a simple PyTorchClassifier for testing.
        :return:
        
tests/metrics/test_metrics.py