code: def decode(
      data_sets: List[bytes],
      message_lens: ndarray,
      inverse_cdf_fun: Callable,
      init_inverse_cdf_states: Any,
  ) -> ndarray:
      """
      Decode a batch of messages via sequential arithmetic coding.
  
      This function decodes a list of byte arrays to a batch of messages into a
      using cumulative distribution Python functionals. Its unique advantage is
      that the CDF can be specified functionally rather than via look-up table,
      which allows use of complicated functionals such as neural networks.
  
      * ``data_sets`` should be a list of Python byte arrays representing
        compressed data.
      * ``inverse_cdf_fun`` should be a callable of the form
        ``f(value, inverse_cdf_state) -> (cdf_low, cdf_high, symbol,
        inverse_cdf_state)``, where `cdf_low` is :math:`P(x <= symbol)` and
        ``cdf_high`` is :math:`P(x <= symbol+1)`. `inverse_cdf_state` can be any
        collection of JAX arrays that is used internally by `inverse_cdf_fun`
        during coding. The CDF probabilities should be on an integer scale
        between ``0` and ``2 ** precision``, where precision is 16 (for a 16-bit
        working integer).
  
      Args:
          data_sets: A list of byte arrays containing compressed messages.
          message_lens: An array of lengths for the decoded messages.
          inverse_cdf_fun: A Python callable that calculates the symbol,
              low, and high CDF cut points based on an input probability.
          init_inverse_cdf_state: The initialization for the state of the inverse
              CDF function (contains CDF array or can be used for conditional
              probabilites).
  
      Returns:
          A batch of decoded messages. If the messages all have the same length,
              an ndarray is returned. Otherwise, the function will return a list
              of ndarray variables.
      """
      max_message_len = jnp.max(message_lens)
  
      def run_decode(data, init_inverse_cdf_state):
          return _single_batch_decode(
              data,
              max_message_len,
              inverse_cdf_fun,
              init_inverse_cdf_state,
          )
  
      map_fun = jax.vmap(run_decode)
  
      # make all byte arrays the same size so we can group them in one jax array
      max_len = max(len(d) for d in data_sets)
      data_sets = jnp.stack(
          [
              jnp.append(
                  jnp.array(bytearray(b), dtype=jnp.uint8),
                  jnp.zeros(max_len - len(b), dtype=jnp.uint8),
              )
              for b in data_sets
          ]
      )
  
      # dispatch
      messages = map_fun(data_sets, init_inverse_cdf_states)
  
      if any([(not ml == max_message_len) for ml in message_lens]):
          messages = [m[:ml] for m, ml in zip(messages, message_lens)]
  
      return messages
  
file_path: NeuralCompression/projects/jax_entropy_coders/jax_arithmetic_coder.py
imports: from typing import Any, Callable, List, Tuple
  import jax
  import jax.lax
  import jax.numpy
  from jax.numpy import ndarray
label_desc: Use image node_id 11 for calling a global function with example usage: decode(data_sets, message_lens, inverse_cdf_fun, init_inverse_cdf_states) and returns: messages, _single_batch_decode