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))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

  using OpRewritePattern<TFL::MeanOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(TFL::MeanOp mean_op,
                                PatternRewriter& rewriter) const override;
};

// Insert Requant ops for reduce_mean.
struct InsertRequantForReduceMean : public OpRewritePattern<TFL::MeanOp> {
  using OpRewritePattern<TFL::MeanOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(TFL::MeanOp mean_op,