code: def get_init_x(
      target: gym.targets.Model,
      num_chains: int,
      num_prior_samples: int = 256,
      method: str = "prior_mean",
  ) -> jnp.ndarray:
      """Returns a 'good' initializer for MCMC chains.
  
      Args:
        target: Target model.
        num_chains: Number of chains to return.
        num_prior_samples: Number of prior samples to use when computing the prior
          mean.
        method: Method to use. Can be either 'prior_mean' or 'z_zero'.
  
      Returns:
        Initial position of the MCMC chain.
      """
      if method == "prior_mean":
          if isinstance(target, gym.targets.VectorModel) and hasattr(
              target.model, "prior_distribution"
          ):
              prior = target.model.prior_distribution()
              try:
                  init_point = target.structured_event_to_vector(
                      prior.mean()
                  )
              except (ValueError, NotImplementedError):
                  prior_samples = target.structured_event_to_vector(
                      prior.sample(
                          num_prior_samples, seed=jax.random.PRNGKey(0)
                      )
                  )
                  init_point = prior_samples.mean(0)
          elif hasattr(target, "prior_distribution"):
              prior = target.prior_distribution()
              try:
                  init_point = prior.mean()
              except (ValueError, NotImplementedError):
                  prior_samples = prior.sample(
                      num_prior_samples, seed=jax.random.PRNGKey(0)
                  )
                  init_point = prior_samples.mean(0)
          else:
              b = target.default_event_space_bijector
              init_point = b(
                  jnp.zeros(
                      b.inverse_event_shape(target.event_shape),
                      target.dtype,
                  )
              )
      elif method == "z_zero":
          b = target.default_event_space_bijector
          init_point = b(
              jnp.zeros(
                  b.inverse_event_shape(target.event_shape),
                  target.dtype,
              )
          )
  
      return jnp.tile(init_point[jnp.newaxis], [num_chains, 1])
  
imports: import functools
  import os
  from typing import Any, Callable, Dict, Optional, Mapping, NamedTuple, Tuple, Union
  from etils import epath
  import gin
  import immutabledict
  import jax
  import jax.numpy
  import numpy
  from discussion.adaptive_malt import utils
  from fun_mc import using_jax
  from inference_gym import using_jax
  import tensorflow_probability.substrates.jax
label_desc: Use image node_id 16 for calling a global function with example usage: get_init_x(target, num_chains, num_prior_samples, method) and returns: jnp