labels = tf.one_hot(features['label'], num_classes)

    with tf.GradientTape() as tape:
      logits = model(inputs)
      loss_value = tf.reduce_mean(
          tf.nn.softmax_cross_entropy_with_logits(labels, logits))

    grads = tape.gradient(loss_value, model.trainable_variables)
    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))

        minutes).
*   Deterministic Op Functionality (enabled by setting `TF_DETERMINISTIC_OPS` to
    `"true"` or `"1"`):
    *   Add a deterministic GPU implementation of
        `tf.nn.softmax_cross_entropy_with_logits`. See PR
        [49178](https://github.com/tensorflow/tensorflow/pull/49178).
    *   Add a deterministic CPU implementation of `tf.image.crop_and_resize`.