Tensorflow 它为什么抱怨'；图像'；变量不可用？

我正在运行TF2教程，并将代码准确复制到.py文件中，然后在PyCharm中运行，但收到以下错误消息：

Testing started at 12:50 AM ...
/home/martin/nlp/my-env/tf/bin/python /home/martin/.local/share/JetBrains/Toolbox/apps/PyCharm-C/ch-0/193.5233.109/plugins/python-ce/helpers/pycharm/_jb_pytest_runner.py --path /home/martin/tf2-tutorial/cnn_mnist.py
Launching pytest with arguments /home/martin/tf2-tutorial/cnn_mnist.py in /home/martin/tf2-tutorial

============================= test session starts ==============================
platform linux -- Python 3.7.1, pytest-5.3.1, py-1.8.0, pluggy-0.13.1 -- /home/martin/nlp/my-env/tf/bin/python
cachedir: .pytest_cache
rootdir: /home/martin/tf2-tutorial
collecting ... collected 1 item

cnn_mnist.py::test2_step ERROR                                           [100%]
test setup failed
file /home/martin/tf2-tutorial/cnn_mnist.py, line 60
  @tf.function
  def test_step(images, labels):
E       fixture 'images' not found
>       available fixtures: cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory
>       use 'pytest --fixtures [testpath]' for help on them.

为什么它认为这是一个pytest程序？为什么会发出这个错误消息？教程应该“按原样”运行

从教程中复制的代码如下（精确副本）：

---

这可能是因为环境问题吗？但是它一直运行得很好？

您的脚本名称是否以

test\uuz

前缀开头？@thushv89，否。它的名称为'cnn\u mnist.py'，您是否使用了运行/调试配置或该命令来运行代码。相反，请尝试保存文件并在Pycharm的终端中运行，因为python cnn_mnist.py inI将大部分代码向后注释并逐渐取消注释，问题就消失了。奇怪！

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)

test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = Conv2D(32, 3, activation='relu')
    self.flatten = Flatten()
    self.d1 = Dense(128, activation='relu')
    self.d2 = Dense(10, activation='softmax')

  def call(self, x):
    x = self.conv1(x)
    x = self.flatten(x)
    x = self.d1(x)
    return self.d2(x)

# Create an instance of the model
model = MyModel()

loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

@tf.function
def train_step(images, labels):
  with tf.GradientTape() as tape:
    predictions = model(images)
    loss = loss_object(labels, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

  train_loss(loss)
  train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
  predictions = model(images)
  t_loss = loss_object(labels, predictions)

  test_loss(t_loss)
  test_accuracy(labels, predictions)

EPOCHS = 5

for epoch in range(EPOCHS):
  for images, labels in train_ds:
    train_step(images, labels)

  for test_images, test_labels in test_ds:
    test_step(test_images, test_labels)

  template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
  print(template.format(epoch+1,
                        train_loss.result(),
                        train_accuracy.result()*100,
                        test_loss.result(),
                        test_accuracy.result()*100))

  # Reset the metrics for the next epoch
  train_loss.reset_states()
  train_accuracy.reset_states()
  test_loss.reset_states()
  test_accuracy.reset_states()