code: def rnn_model_fn(
      features, labels, mode
  ):  # pylint: disable=unused-argument
      """Model function for a RNN."""
  
      # Define RNN architecture using tf.keras.layers.
      x = features["x"]
      x = tf.reshape(x, [-1, SEQ_LEN])
      input_layer = x[:, :-1]
      input_one_hot = tf.one_hot(input_layer, 256)
      lstm = tf.keras.layers.LSTM(256, return_sequences=True)(
          input_one_hot
      )
      logits = tf.keras.layers.Dense(256)(lstm)
  
      # Calculate loss as a vector (to support microbatches in DP-SGD).
      vector_loss = tf.nn.softmax_cross_entropy_with_logits(
          labels=tf.cast(tf.one_hot(x[:, 1:], 256), dtype=tf.float32),
          logits=logits,
      )
      # Define mean of loss across minibatch (for reporting through tf.Estimator).
      scalar_loss = tf.reduce_mean(vector_loss)
  
      # Configure the training op (for TRAIN mode).
      if mode == tf_estimator.ModeKeys.TRAIN:
          if FLAGS.dpsgd:
              optimizer = dp_optimizer.DPAdamGaussianOptimizer(
                  l2_norm_clip=FLAGS.l2_norm_clip,
                  noise_multiplier=FLAGS.noise_multiplier,
                  num_microbatches=FLAGS.microbatches,
                  learning_rate=FLAGS.learning_rate,
                  unroll_microbatches=True,
              )
              opt_loss = vector_loss
          else:
              optimizer = tf.compat.v1.train.AdamOptimizer(
                  learning_rate=FLAGS.learning_rate
              )
              opt_loss = scalar_loss
          global_step = tf.compat.v1.train.get_global_step()
          train_op = optimizer.minimize(
              loss=opt_loss, global_step=global_step
          )
          return tf_estimator.EstimatorSpec(
              mode=mode, loss=scalar_loss, train_op=train_op
          )
  
      # Add evaluation metrics (for EVAL mode).
      elif mode == tf_estimator.ModeKeys.EVAL:
          eval_metric_ops = {
              "accuracy": tf.metrics.accuracy(
                  labels=tf.cast(x[:, 1:], dtype=tf.int32),
                  predictions=tf.argmax(input=logits, axis=2),
              )
          }
          return tf_estimator.EstimatorSpec(
              mode=mode,
              loss=scalar_loss,
              eval_metric_ops=eval_metric_ops,
          )
  
file_path: privacy/tutorials/lm_dpsgd_tutorial.py
imports: import os
  from absl import app
  from absl import flags
  from absl import logging
  import dp_accounting
  import numpy
  import tensorflow
  from tensorflow import estimator
  from tensorflow.compat.v1 import estimator
  import tensorflow_datasets
  from tensorflow_privacy.privacy.optimizers import dp_optimizer
label_desc: Use image node_id 1 for calling a global function with example usage: rnn_model_fn(features, labels, mode) and returns: tf_estimator, tf_estimator