根据讨论，模型确实是这个错误的原因。下面的更正是这个问题的工作运行代码。
在数据集中，将数据类型***int***更改为*float*将防止将来发生TypeError。

from __future__ import absolute_import, division, print_function, unicode_literals
!pip install tf-nightly
#%tensorflow_version 2.x
import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import datasets, layers, models, Model
import numpy as np

mirrored_strategy = tf.distribute.MirroredStrategy()

def train_gen():
    for i in range(10):
      yield tf.constant(i, shape=(4,4,4,1)), tf.constant(i, shape=(4,4,4,1))

def test_gen():
    for i in range(2):
      yield tf.constant(i+10, shape=(4,4,4,1)), tf.constant(i+10, shape=(4,4,4,1))

BATCH_SIZE_PER_REPLICA = 2
GLOBAL_BATCH_SIZE = BATCH_SIZE_PER_REPLICA * mirrored_strategy.num_replicas_in_sync

train_ds = tf.data.Dataset.from_generator(
    train_gen,
    output_types=(tf.float32, tf.float32),    # using float as your datatype
    output_shapes=((4,4,4,1), (4,4,4,1))
)

test_ds = tf.data.Dataset.from_generator(
    test_gen,
    output_types=(tf.float32, tf.float32),      # using float as your datatype
    output_shapes=((4,4,4,1), (4,4,4,1))
)

train_ds = train_ds.batch(GLOBAL_BATCH_SIZE)
test_ds = test_ds.batch(GLOBAL_BATCH_SIZE)

在您的模型上，***mirrored_strategy.scope（）***导致了您遇到的错误。将其作为下面的代码删除将解决此问题。

class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        #with mirrored_strategy.scope():
        self.cnn_down_1 = layers.Conv3D(1, (2, 2, 2), strides=2, padding='same')
        self.cnn_up_1 = layers.Conv3DTranspose(1, (2, 2, 2), strides=2, padding='same')

    def call(self, inputs):
         #with mirrored_strategy.scope():
            x = self.cnn_down_1(inputs)
            return self.cnn_up_1(x) 

assert tf.distribute.get_replica_context() is not None  # default

在下面的代码部分，需要删除train_step和test_step函数之前的***@tf.function***。

with mirrored_strategy.scope():
    #assert tf.distribute.get_replica_context() is not None  # default
    loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=True,
    reduction=tf.keras.losses.Reduction.NONE)
    def loss_object(target_y, pred_y):
        pred_ssum = tf.math.reduce_sum(tf.math.square(pred_y))
        target_ssum = tf.math.reduce_sum(tf.math.square(target_y))
        mul_sum = tf.math.reduce_sum(tf.math.multiply(pred_y, target_y))
        return tf.math.divide(-2 * mul_sum, tf.math.add(pred_ssum, target_ssum))

    EPOCHS = 10


    model = MyModel()

    optimizer = tf.keras.optimizers.RMSprop(lr=2e-5)

    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, training=True)
            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.update_state(labels, predictions)
        return loss

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

        test_loss.update_state(t_loss)
        test_accuracy.update_state(labels, predictions)

    @tf.function
    def distributed_train_step(images, labels):
      assert tf.distribute.get_replica_context() is None
      per_replica_losses = mirrored_strategy.experimental_run_v2(train_step, args=(images, labels,))
      return mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses,
                          axis=None)

    @tf.function
    def distributed_test_step(images, labels):
        return mirrored_strategy.experimental_run_v2(test_step, args=(images, labels,))

    for epoch in range(EPOCHS):
    # Reset the metrics at the start of the next epoch
        #train_loss.reset_states()
        total_loss = 0.0
        num_batches = 0

        for images, labels in train_ds:
            #train_step(images, labels)
            total_loss += distributed_train_step(images, labels)
            num_batches += 1
        train_loss = total_loss/num_batches

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

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

        train_accuracy.reset_states()
        test_loss.reset_states()
        test_accuracy.reset_states()

这解决了问题，并正确地删除了错误。这现在正在工作。希望这解决了问题。

我也有类似的错误说RuntimeError:apply_gradients() cannot be called in cross-replica context. Use tf.distribute.Strategy.run to enter replica context.的错误改变时，我删除@tf.function`作为@TF_支持说，在他的答案。