code: def fixture_fashion_mnist():
      """fixture_fashion_mnist"""
      classes = [
          "T-shirt/top",
          "Trouser",
          "Pullover",
          "Dress",
          "Coat",
          "Sandal",
          "Shirt",
          "Sneaker",
          "Bag",
          "Ankle boot",
      ]
  
      class MNISTClassNamesLayer(tf.keras.layers.Layer):
          """MNISTClassNamesLayer"""
  
          def __init__(self):
              self._classes = tf.constant(classes)
              super().__init__(trainable=False)
  
          def call(self, inputs):  # pylint: disable=arguments-differ
              content = tf.argmax(inputs, axis=1)
              content = tf.gather(self._classes, content)
              return content
  
      (
          (train_images, train_labels),
          (test_images, _),
      ) = tf.keras.datasets.fashion_mnist.load_data()
  
      train_images = train_images / 255.0
      test_images = test_images / 255.0
  
      model = tf.keras.Sequential(
          [
              tf.keras.layers.Flatten(input_shape=(28, 28)),
              tf.keras.layers.Dense(128, activation=tf.nn.relu),
              tf.keras.layers.Dense(10, activation=tf.nn.softmax),
          ]
      )
  
      model.compile(
          optimizer="adam",
          loss="sparse_categorical_crossentropy",
          metrics=["accuracy"],
      )
  
      model.fit(train_images, train_labels, epochs=5)
  
      predictions = model.predict(test_images)
  
      predictions = [classes[v] for v in np.argmax(predictions, axis=1)]
  
      return model, test_images, predictions, MNISTClassNamesLayer()
  
file_path: io/tests/test_io_layer.py
imports: import os
  import sys
  import time
  import tempfile
  import pytest
  import numpy
  import tensorflow
  import tensorflow_io
label_desc: Use image node_id 1 for calling a global function with example usage: fixture_fashion_mnist() and returns: model,  test_images,  predictions,  MNISTClassNamesLayer, content