code: def init_tfp_randomwalkmetropolis(
      target_log_prob_fn,
      step_size,
      store_parameters_in_results=False,
      num_leapfrog_steps=None,
  ):  # pylint: disable=unused-argument
      return random_walk_metropolis.RandomWalkMetropolis(
          target_log_prob_fn,
          new_state_fn=random_walk_metropolis.random_walk_normal_fn(
              scale=step_size
          ),
      )
  
imports: from absl import logging
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_combinations
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import unnest
  from tensorflow_probability.python.mcmc import diagnostic
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import random_walk_metropolis
  from tensorflow_probability.python.mcmc import replica_exchange_mc
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import simple_step_size_adaptation
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 1 for calling a global function with example usage: init_tfp_randomwalkmetropolis(target_log_prob_fn, step_size, store_parameters_in_results, num_leapfrog_steps) and returns: random_walk_metropolis