code: def _run_trial_impl(
      seed,
      inits,
      ground_truth,
      target,
      method,
      num_adaptation_steps,
      num_results,
      num_chains,
      jitter_style,
      adapt_normalization_power,
      target_accept_prob,
      step_size_adaptation_rate_decay,
      trajectory_length_adaptation_rate_decay,
      save_warmup,
  ):
      """Implementation of run_trial."""
      init_z = inits["init_z"]
      if num_chains is not None:
          if num_chains <= init_z.shape[0]:
              init_z = init_z[:num_chains]
          else:
              init_z = jnp.repeat(
                  init_z, int(np.ceil(num_chains / init_z.shape[0])), 0
              )
              init_z = init_z[:num_chains]
      init_x = target.default_event_space_bijector.forward(init_z)
  
      if method == "meads":
          trace, final = run_meads_on_target(
              target=target,
              init_x=init_x,
              seed=seed,
              num_adaptation_steps=num_adaptation_steps,
              num_results=num_results,
              thinning=10,
              num_folds=4,
              step_size_multiplier=0.5,
              damping_slowdown=1.0,
              ground_truth=ground_truth,
              save_warmup=save_warmup,
          )
      elif method == "nuts":
          trace, final = run_adaptive_nuts_on_target(
              target=target,
              init_x=init_x,
              seed=seed,
              num_adaptation_steps=num_adaptation_steps,
              num_results=num_results,
              rvar_factor=8,
              rvar_smoothing=1,
              init_step_size=1e-2,
              step_size_adaptation_rate_decay=step_size_adaptation_rate_decay,
              target_accept_prob=0.7,
              ground_truth=ground_truth,
              save_warmup=save_warmup,
          )
      else:
          trace, final = run_adaptive_mcmc_on_target(
              target=target,
              init_x=init_x,
              method=method,
              seed=seed,
              num_adaptation_steps=num_adaptation_steps,
              num_results=num_results,
              init_step_size=1e-2,
              trajectory_length_adaptation_rate=0.05,
              trajectory_length_adaptation_rate_decay=trajectory_length_adaptation_rate_decay,
              step_size_adaptation_rate_decay=step_size_adaptation_rate_decay,
              target_accept_prob=target_accept_prob,
              adapt_normalization_power=adapt_normalization_power,
              use_precond_eigenvalue=True,
              trajectory_opt_kwargs={
                  "beta_1": 0.0,
                  "beta_2": 0.95,
                  "epsilon": 1e-8,
              },
              rvar_factor=8,
              principal_factor=3,
              rvar_smoothing=1,
              jitter_style=jitter_style,
              ground_truth=ground_truth,
              save_warmup=save_warmup,
          )
  
      res = {
          "stats": final["stats"],
          "num_integrator_steps": trace["num_integrator_steps"],
          "scalar_step_size": trace["scalar_step_size"],
          "vector_step_size": trace["vector_step_size"][-1],
      }
      if save_warmup:
          rel_max_m2_bias_sq = (
              final["bias"]["m2"]["bias_sq"]
              / ground_truth["stats"]["m2"]["mean"] ** 2
          ).max(-1)
          res["rel_max_m2_bias_sq"] = rel_max_m2_bias_sq
      if "max_eigenvalue" in trace:
          res["max_eigenvalue"] = trace["max_eigenvalue"]
      if "mean_trajectory_length" in trace:
          res["mean_trajectory_length"] = trace[
              "mean_trajectory_length"
          ]
      if "damping" in trace and trace["damping"] is not None:
          res["damping"] = trace["damping"]
      if "mean_num_integrator_steps" in trace:
          res["mean_num_integrator_steps"] = trace[
              "mean_num_integrator_steps"
          ]
      return res
  
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 24 for calling a global function with example usage: _run_trial_impl(seed, inits, ground_truth, target, method, num_adaptation_steps, num_results, num_chains, jitter_style, adapt_normalization_power, target_accept_prob, step_size_adaptation_rate_decay, trajectory_length_adaptation_rate_decay, save_warmup) and returns: res