code: def initialize_model(
      model,
      model_args=(),
      model_kwargs={},
      transforms=None,
      max_plate_nesting=None,
      jit_compile=False,
      jit_options=None,
      skip_jit_warnings=False,
      num_chains=1,
      init_strategy=init_to_uniform,
      initial_params=None,
  ):
      """
      Given a Python callable with Pyro primitives, generates the following model-specific
      properties needed for inference using HMC/NUTS kernels:
  
      - initial parameters to be sampled using a HMC kernel,
      - a potential function whose input is a dict of parameters in unconstrained space,
      - transforms to transform latent sites of `model` to unconstrained space,
      - a prototype trace to be used in MCMC to consume traces from sampled parameters.
  
      :param model: a Pyro model which contains Pyro primitives.
      :param tuple model_args: optional args taken by `model`.
      :param dict model_kwargs: optional kwargs taken by `model`.
      :param dict transforms: Optional dictionary that specifies a transform
          for a sample site with constrained support to unconstrained space. The
          transform should be invertible, and implement `log_abs_det_jacobian`.
          If not specified and the model has sites with constrained support,
          automatic transformations will be applied, as specified in
          :mod:`torch.distributions.constraint_registry`.
      :param int max_plate_nesting: Optional bound on max number of nested
          :func:`pyro.plate` contexts. This is required if model contains
          discrete sample sites that can be enumerated over in parallel.
      :param bool jit_compile: Optional parameter denoting whether to use
          the PyTorch JIT to trace the log density computation, and use this
          optimized executable trace in the integrator.
      :param dict jit_options: A dictionary contains optional arguments for
          :func:`torch.jit.trace` function.
      :param bool ignore_jit_warnings: Flag to ignore warnings from the JIT
          tracer when ``jit_compile=True``. Default is False.
      :param int num_chains: Number of parallel chains. If `num_chains > 1`,
          the returned `initial_params` will be a list with `num_chains` elements.
      :param callable init_strategy: A per-site initialization function.
          See :ref:`autoguide-initialization` section for available functions.
      :param dict initial_params: dict containing initial tensors in unconstrained
          space to initiate the markov chain.
      :returns: a tuple of (`initial_params`, `potential_fn`, `transforms`, `prototype_trace`)
      """
      # XXX `transforms` domains are sites' supports
      # FIXME: find a good pattern to deal with `transforms` arg
      if transforms is None:
          automatic_transform_enabled = True
          transforms = {}
      else:
          automatic_transform_enabled = False
      if max_plate_nesting is None:
          max_plate_nesting = _guess_max_plate_nesting(
              model, model_args, model_kwargs
          )
      # Wrap model in `poutine.enum` to enumerate over discrete latent sites.
      # No-op if model does not have any discrete latents.
      model = poutine.enum(
          config_enumerate(model),
          first_available_dim=-1 - max_plate_nesting,
      )
      prototype_model = poutine.trace(
          InitMessenger(init_strategy)(model)
      )
      model_trace = prototype_model.get_trace(
          *model_args, **model_kwargs
      )
      has_enumerable_sites = False
      prototype_samples = {}
      for name, node in model_trace.iter_stochastic_nodes():
          fn = node["fn"]
          if isinstance(fn, _Subsample):
              if (
                  fn.subsample_size is not None
                  and fn.subsample_size < fn.size
              ):
                  raise NotImplementedError(
                      "HMC/NUTS does not support model with subsample sites."
                  )
              continue
          if node["fn"].has_enumerate_support:
              has_enumerable_sites = True
              continue
          # we need to detach here because this sample can be a leaf variable,
          # so we can't change its requires_grad flag to calculate its grad in
          # velocity_verlet
          prototype_samples[name] = node["value"].detach()
          if automatic_transform_enabled:
              transforms[name] = biject_to(node["fn"].support).inv
  
      trace_prob_evaluator = TraceEinsumEvaluator(
          model_trace, has_enumerable_sites, max_plate_nesting
      )
  
      pe_maker = _PEMaker(
          model,
          model_args,
          model_kwargs,
          trace_prob_evaluator,
          transforms,
      )
  
      if initial_params is None:
          prototype_params = {
              k: transforms[k](v) for k, v in prototype_samples.items()
          }
          # Note that we deliberately do not exercise jit compilation here so as to
          # enable potential_fn to be picklable (a torch._C.Function cannot be pickled).
          # We pass model_trace merely for computational savings.
          initial_params = _find_valid_initial_params(
              model,
              model_args,
              model_kwargs,
              transforms,
              pe_maker.get_potential_fn(),
              prototype_params,
              num_chains=num_chains,
              init_strategy=init_strategy,
              trace=model_trace,
          )
      potential_fn = pe_maker.get_potential_fn(
          jit_compile, skip_jit_warnings, jit_options
      )
      return initial_params, potential_fn, transforms, model_trace
  
imports: import functools
  import traceback
  import warnings
  from collections import OrderedDict, defaultdict
  from functools import partial, reduce
  from itertools import product
  import torch
  from opt_einsum import shared_intermediates
  from torch.distributions import biject_to
  import pyro
  import pyro.poutine
  from pyro.distributions.util import broadcast_shape, logsumexp
  from pyro.infer import config_enumerate
  from pyro.infer.autoguide.initialization import InitMessenger, init_to_uniform
  from pyro.infer.util import is_validation_enabled
  from pyro.ops import stats
  from pyro.ops.contract import contract_to_tensor
  from pyro.ops.integrator import potential_grad
  from pyro.poutine.subsample_messenger import _Subsample
  from pyro.poutine.util import prune_subsample_sites
  from pyro.util import check_site_shape, ignore_jit_warnings
label_desc: Use image node_id 3 for calling a global function with example usage: initialize_model(model, model_args, model_kwargs, transforms, max_plate_nesting, jit_compile, jit_options, skip_jit_warnings, num_chains, init_strategy, initial_params) and returns: initial_params,  potential_fn,  transforms,  model_trace