def random_inputs(
    input_shape=gin.REQUIRED, input_dtype=jnp.int32, input_range=(0, 255),
    output_shape=gin.REQUIRED, output_dtype=jnp.int32, output_range=(0, 9)):
  """Make random Inputs for debugging.

  Args:
    input_shape: the shape of inputs (including batch dimension).
    input_dtype: the type of the inputs (int32 by default).
    input_range: the range of inputs (defaults to (0, 255)).
    output_shape: the shape of outputs (including batch dimension).
    output_dtype: the type of the outputs (int32 by default).
    output_range: the range of outputs (defaults to (0, 9)).

  Returns:
    trax.inputs.Inputs
  """
  def random_minibatches(n_devices):
    """Generate a stream of random mini-batches."""
    assert input_range[0] % n_devices == 0
    if input_dtype in [jnp.float16, jnp.float32, jnp.float64]:
      rand = np.random.uniform
    else:
      rand = np.random.random_integers
    while True:
      inp = rand(input_range[0], input_range[1], input_shape)
      inp = inp.astype(input_dtype)
      out = rand(output_range[0], output_range[1], output_shape)
      out = out.astype(output_dtype)
      yield inp, out

  return Inputs(random_minibatches)
Make random Inputs for debugging.

  Args:
    input_shape: the shape of inputs (including batch dimension).
    input_dtype: the type of the inputs (int32 by default).
    input_range: the range of inputs (defaults to (0, 255)).
    output_shape: the shape of outputs (including batch dimension).
    output_dtype: the type of the outputs (int32 by default).
    output_range: the range of outputs (defaults to (0, 9)).

  Returns:
    trax.inputs.Inputs
  
trax/data/inputs.py