code: def main(strategy):
      """Trains an MNIST model using the given tf.distribute.Strategy."""
      # TODO(fengliuai): put this in some automatically generated code.
      os.environ[
          "TF_MLIR_TFR_LIB_DIR"
      ] = "tensorflow/compiler/mlir/tfr/examples/mnist"
  
      ds_train = tfds.load("mnist", split="train", shuffle_files=True)
      ds_train = ds_train.shuffle(1024).batch(batch_size).prefetch(64)
      ds_train = strategy.experimental_distribute_dataset(ds_train)
  
      with strategy.scope():
          # Create an mnist float model with the specified float state.
          model = FloatModel()
          optimizer = tf.keras.optimizers.Adam(
              learning_rate=learning_rate
          )
  
      def train_step(features):
          inputs = tf.image.convert_image_dtype(
              features["image"], dtype=tf.float32, saturate=False
          )
          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)
          )
          accuracy = tf.reduce_mean(
              tf.cast(correct_prediction, tf.float32)
          )
          optimizer.apply_gradients(
              zip(grads, model.trainable_variables)
          )
          return accuracy, loss_value
  
      @tf.function
      def distributed_train_step(dist_inputs):
          per_replica_accuracy, per_replica_losses = strategy.run(
              train_step, args=(dist_inputs,)
          )
          accuracy = strategy.reduce(
              tf.distribute.ReduceOp.MEAN,
              per_replica_accuracy,
              axis=None,
          )
          loss_value = strategy.reduce(
              tf.distribute.ReduceOp.MEAN, per_replica_losses, axis=None
          )
          return accuracy, loss_value
  
      iterator = iter(ds_train)
      accuracy = 0.0
      for step in range(flags.FLAGS.train_steps):
          accuracy, loss_value = distributed_train_step(next(iterator))
          if step % display_step == 0:
              tf.print("Step %d:" % step)
              tf.print("    Loss = %f" % loss_value)
              tf.print("    Batch accuracy = %f" % accuracy)
  
      return accuracy
  
file_path: tensorflow-upstream/tensorflow/compiler/mlir/tfr/examples/mnist/mnist_train.py
imports: import os
  from absl import flags
  import tensorflow
  import tensorflow_datasets
  from tensorflow.compiler.mlir.tfr.examples.mnist import gen_mnist_ops
  from tensorflow.compiler.mlir.tfr.examples.mnist import ops_defs
  from tensorflow.python.framework import load_library
label_desc: Use image node_id 1 for calling a global function with example usage: main(strategy) and returns: accuracy, accuracy,  loss_value, accuracy,  loss_value