code: def _prepare_args(
      target_log_prob_fn,
      state,
      step_size,
      target_log_prob=None,
      maybe_expand=False,
      description="target_log_prob",
  ):
      """Processes input args to meet list-like assumptions."""
      dtype = dtype_util.common_dtype(
          [state, step_size], dtype_hint=tf.float32
      )
      state_parts = (
          list(state) if mcmc_util.is_list_like(state) else [state]
      )
      state_parts = [
          tf.convert_to_tensor(s, name="current_state", dtype=dtype)
          for s in state_parts
      ]
  
      target_log_prob = _maybe_call_fn(
          target_log_prob_fn, state_parts, target_log_prob, description
      )
      step_sizes = (
          list(step_size)
          if mcmc_util.is_list_like(step_size)
          else [step_size]
      )
      step_sizes = [
          tf.convert_to_tensor(value=s, name="step_size", dtype=dtype)
          for s in step_sizes
      ]
      if len(step_sizes) == 1:
          step_sizes *= len(state_parts)
      if len(state_parts) != len(step_sizes):
          raise ValueError(
              "There should be exactly one `step_size` or it should "
              "have same length as `current_state`."
          )
  
      def maybe_flatten(x):
          return (
              x
              if maybe_expand or mcmc_util.is_list_like(state)
              else x[0]
          )
  
      return [
          maybe_flatten(state_parts),
          maybe_flatten(step_sizes),
          target_log_prob,
      ]
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc.internal import slice_sampler_utils
  from tensorflow_probability.python.mcmc.internal import util
  from tensorflow_probability.python.random import random_ops
label_desc: Use image node_id 5 for calling a global function with example usage: _prepare_args(target_log_prob_fn, state, step_size, target_log_prob, maybe_expand, description) and returns: list, unknown