code: def compute_hmc_step_size(scalings, state_std, num_leapfrog_steps):
      return [
          s
          / ps.cast(num_leapfrog_steps, s.dtype)
          * ps.cast(  # pylint: disable=g-complex-comprehension
              bu.left_justified_expand_dims_like(scalings, s), s.dtype
          )
          for s in state_std
      ]
  
imports: import collections
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc import weighted_resampling
  from tensorflow_probability.python.internal import broadcast_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.math.generic import log1mexp
  from tensorflow_probability.python.math.generic import log_add_exp
  from tensorflow_probability.python.math.generic import reduce_logmeanexp
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import random_walk_metropolis
  from tensorflow_probability.python.mcmc import transformed_kernel
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 4 for calling a global function with example usage: compute_hmc_step_size(scalings, state_std, num_leapfrog_steps) and returns: unknown