code: def sample_numpyro_nuts(
      draws: int = 1000,
      tune: int = 1000,
      chains: int = 4,
      target_accept: float = 0.8,
      random_seed: Optional[RandomState] = None,
      initvals: Optional[
          Union[StartDict, Sequence[Optional[StartDict]]]
      ] = None,
      model: Optional[Model] = None,
      var_names: Optional[Sequence[str]] = None,
      progressbar: bool = True,
      keep_untransformed: bool = False,
      chain_method: str = "parallel",
      postprocessing_backend: Optional[Literal["cpu", "gpu"]] = None,
      postprocessing_vectorize: Literal["vmap", "scan"] = "scan",
      idata_kwargs: Optional[Dict] = None,
      nuts_kwargs: Optional[Dict] = None,
      postprocessing_chunks=None,
  ) -> az.InferenceData:
      """
      Draw samples from the posterior using the NUTS method from the ``numpyro`` library.
  
      Parameters
      ----------
      draws : int, default 1000
          The number of samples to draw. The number of tuned samples are discarded by
          default.
      tune : int, default 1000
          Number of iterations to tune. Samplers adjust the step sizes, scalings or
          similar during tuning. Tuning samples will be drawn in addition to the number
          specified in the ``draws`` argument.
      chains : int, default 4
          The number of chains to sample.
      target_accept : float in [0, 1].
          The step size is tuned such that we approximate this acceptance rate. Higher
          values like 0.9 or 0.95 often work better for problematic posteriors.
      random_seed : int, RandomState or Generator, optional
          Random seed used by the sampling steps.
      initvals: StartDict or Sequence[Optional[StartDict]], optional
          Initial values for random variables provided as a dictionary (or sequence of
          dictionaries) mapping the random variable (by name or reference) to desired
          starting values.
      model : Model, optional
          Model to sample from. The model needs to have free random variables. When inside
          a ``with`` model context, it defaults to that model, otherwise the model must be
          passed explicitly.
      var_names : sequence of str, optional
          Names of variables for which to compute the posterior samples. Defaults to all
          variables in the posterior.
      progressbar : bool, default True
          Whether or not to display a progress bar in the command line. The bar shows the
          percentage of completion, the sampling speed in samples per second (SPS), and
          the estimated remaining time until completion ("expected time of arrival"; ETA).
      keep_untransformed : bool, default False
          Include untransformed variables in the posterior samples. Defaults to False.
      chain_method : str, default "parallel"
          Specify how samples should be drawn. The choices include "sequential",
          "parallel", and "vectorized".
      postprocessing_backend: Optional[Literal["cpu", "gpu"]], default None,
          Specify how postprocessing should be computed. gpu or cpu
      postprocessing_vectorize: Literal["vmap", "scan"], default "scan"
          How to vectorize the postprocessing: vmap or sequential scan
      idata_kwargs : dict, optional
          Keyword arguments for :func:`arviz.from_dict`. It also accepts a boolean as
          value for the ``log_likelihood`` key to indicate that the pointwise log
          likelihood should not be included in the returned object. Values for
          ``observed_data``, ``constant_data``, ``coords``, and ``dims`` are inferred from
          the ``model`` argument if not provided in ``idata_kwargs``. If ``coords`` and
          ``dims`` are provided, they are used to update the inferred dictionaries.
      nuts_kwargs: dict, optional
          Keyword arguments for :func:`numpyro.infer.NUTS`.
  
      Returns
      -------
      InferenceData
          ArviZ ``InferenceData`` object that contains the posterior samples, together
          with their respective sample stats and pointwise log likeihood values (unless
          skipped with ``idata_kwargs``).
      """
      if postprocessing_chunks is not None:
          import warnings
  
          warnings.warn(
              "postprocessing_chunks is deprecated due to being unstable, "
              "using postprocessing_vectorize='scan' instead",
              DeprecationWarning,
          )
      import numpyro
  
      from numpyro.infer import MCMC, NUTS
  
      model = modelcontext(model)
  
      if var_names is None:
          var_names = model.unobserved_value_vars
  
      vars_to_sample = list(
          get_default_varnames(
              var_names, include_transformed=keep_untransformed
          )
      )
  
      (random_seed,) = _get_seeds_per_chain(random_seed, 1)
  
      tic1 = datetime.now()
      logger.info("Compiling...")
  
      init_params = _get_batched_jittered_initial_points(
          model=model,
          chains=chains,
          initvals=initvals,
          random_seed=random_seed,
      )
  
      logp_fn = get_jaxified_logp(model, negative_logp=False)
  
      nuts_kwargs = _update_numpyro_nuts_kwargs(nuts_kwargs)
      nuts_kernel = NUTS(
          potential_fn=logp_fn,
          target_accept_prob=target_accept,
          **nuts_kwargs,
      )
  
      pmap_numpyro = MCMC(
          nuts_kernel,
          num_warmup=tune,
          num_samples=draws,
          num_chains=chains,
          postprocess_fn=None,
          chain_method=chain_method,
          progress_bar=progressbar,
      )
  
      tic2 = datetime.now()
      logger.info(f"Compilation time = {tic2 - tic1}")
  
      logger.info("Sampling...")
  
      map_seed = jax.random.PRNGKey(random_seed)
      if chains > 1:
          map_seed = jax.random.split(map_seed, chains)
  
      pmap_numpyro.run(
          map_seed,
          init_params=init_params,
          extra_fields=(
              "num_steps",
              "potential_energy",
              "energy",
              "adapt_state.step_size",
              "accept_prob",
              "diverging",
          ),
      )
  
      raw_mcmc_samples = pmap_numpyro.get_samples(group_by_chain=True)
  
      tic3 = datetime.now()
      logger.info(f"Sampling time = {tic3 - tic2}")
  
      logger.info("Transforming variables...")
      jax_fn = get_jaxified_graph(
          inputs=model.value_vars, outputs=vars_to_sample
      )
      result = _postprocess_samples(
          jax_fn,
          raw_mcmc_samples,
          postprocessing_backend=postprocessing_backend,
          postprocessing_vectorize=postprocessing_vectorize,
      )
      mcmc_samples = {v.name: r for v, r in zip(vars_to_sample, result)}
  
      tic4 = datetime.now()
      logger.info(f"Transformation time = {tic4 - tic3}")
  
      if idata_kwargs is None:
          idata_kwargs = {}
      else:
          idata_kwargs = idata_kwargs.copy()
  
      if idata_kwargs.pop("log_likelihood", False):
          tic5 = datetime.now()
          logger.info("Computing Log Likelihood...")
          log_likelihood = _get_log_likelihood(
              model,
              raw_mcmc_samples,
              backend=postprocessing_backend,
              postprocessing_vectorize=postprocessing_vectorize,
          )
          tic6 = datetime.now()
          logger.info(
              f"Log Likelihood time = {tic6 - tic5}",
          )
      else:
          log_likelihood = None
  
      attrs = {
          "sampling_time": (tic3 - tic2).total_seconds(),
      }
  
      coords, dims = coords_and_dims_for_inferencedata(model)
      # Update 'coords' and 'dims' extracted from the model with user 'idata_kwargs'
      # and drop keys 'coords' and 'dims' from 'idata_kwargs' if present.
      _update_coords_and_dims(
          coords=coords, dims=dims, idata_kwargs=idata_kwargs
      )
      # Use 'partial' to set default arguments before passing 'idata_kwargs'
      to_trace = partial(
          az.from_dict,
          log_likelihood=log_likelihood,
          observed_data=find_observations(model),
          constant_data=find_constants(model),
          sample_stats=_sample_stats_to_xarray(pmap_numpyro),
          coords=coords,
          dims=dims,
          attrs=make_attrs(attrs, library=numpyro),
      )
      az_trace = to_trace(posterior=mcmc_samples, **idata_kwargs)
      return az_trace
  
imports: import logging
  import os
  import re
  from datetime import datetime
  from functools import partial
  from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Union
  import arviz
  import jax
  import jax.numpy
  import numpy
  import pytensor.tensor
  from arviz.data.base import make_attrs
  from jax.lax import scan
  from pytensor.compile import SharedVariable, Supervisor, mode
  from pytensor.graph.basic import graph_inputs
  from pytensor.graph.fg import FunctionGraph
  from pytensor.graph.replace import clone_replace
  from pytensor.link.jax.dispatch import jax_funcify
  from pytensor.raise_op import Assert
  from pytensor.tensor import TensorVariable
  from pytensor.tensor.random.type import RandomType
  from pymc import Model, modelcontext
  from pymc.backends.arviz import coords_and_dims_for_inferencedata, find_constants, find_observations
  from pymc.distributions.multivariate import PosDefMatrix
  from pymc.initial_point import StartDict
  from pymc.logprob.utils import CheckParameterValue
  from pymc.sampling.mcmc import _init_jitter
  from pymc.util import RandomSeed, RandomState, _get_seeds_per_chain, get_default_varnames
label_desc: Use image node_id 17 for calling a global function with example usage: sample_numpyro_nuts(draws, tune, chains, target_accept, random_seed, initvals, model, var_names, progressbar, keep_untransformed, chain_method, postprocessing_backend, postprocessing_vectorize, idata_kwargs, nuts_kwargs, postprocessing_chunks) and returns: az_trace