code: def random_inputs(seq_len, batch_size):
      def stream_fn(num_devices):
          del num_devices
          while True:
              x = np.random.uniform(size=(batch_size, seq_len))
              y = np.random.uniform(size=(batch_size, seq_len))
              mask = np.ones_like(x).astype(np.float32)
              yield (x, y, x, mask)
  
      return inputs.Inputs(
          train_stream=stream_fn,
          eval_stream=stream_fn,
      )
  
imports: import functools
  import io
  from unittest import mock
  from absl.testing import absltest
  from absl.testing import parameterized
  import gym
  import numpy
  from trax import models
  from trax import test_utils
  from trax.data import inputs
  from trax.layers import test_utils
  from trax.rl import serialization_utils
  from trax.rl import space_serializer
  from trax.supervised import callbacks
  from trax.supervised import lr_schedules
  from trax.supervised import trainer_lib
  from trax.supervised import training
label_desc: Use image node_id 1 for calling a global function with example usage: random_inputs(seq_len, batch_size) and returns: inputs