code: def train_bit_shift(seq_length, num_iterations, print_every_n):
      optimizer = tf.keras.optimizers.Adam(1e-3)
  
      model = TrXLNet(
          observation_space=Box(
              low=0, high=1, shape=(1,), dtype=np.int32
          ),
          action_space=Discrete(2),
          num_outputs=2,
          model_config={"max_seq_len": seq_length},
          name="trxl",
          num_transformer_units=1,
          attention_dim=10,
          num_heads=5,
          head_dim=20,
          position_wise_mlp_dim=20,
      )
  
      shift = 10
      train_batch = 10
      test_batch = 100
      data_gen = bit_shift_generator(
          seq_length, shift=shift, batch_size=train_batch
      )
      test_gen = bit_shift_generator(
          seq_length, shift=shift, batch_size=test_batch
      )
  
      @tf.function
      def update_step(inputs, targets):
          model_out = model(
              {"obs": inputs},
              state=[tf.reshape(inputs, [-1, seq_length, 1])],
              seq_lens=np.full(
                  shape=(train_batch,), fill_value=seq_length
              ),
          )
          optimizer.minimize(
              lambda: train_loss(targets, model_out),
              lambda: model.trainable_variables,
          )
  
      for i, (inputs, targets) in zip(range(num_iterations), data_gen):
          inputs_in = np.reshape(inputs, [-1, 1])
          targets_in = np.reshape(targets, [-1])
          update_step(
              tf.convert_to_tensor(inputs_in),
              tf.convert_to_tensor(targets_in),
          )
  
          if i % print_every_n == 0:
              test_inputs, test_targets = next(test_gen)
              print(i, train_loss(test_targets, model(test_inputs)))
  
imports: from gymnasium.spaces import Box, Discrete
  import numpy
  from rllib.models.tf.attention_net import TrXLNet
  from ray.rllib.utils.framework import try_import_tf
label_desc: Use image node_id 3 for calling a global function with example usage: train_bit_shift(seq_length, num_iterations, print_every_n) without return types