code: def render_model(
      model: Callable,
      model_args: Optional[Union[tuple, List[tuple]]] = None,
      model_kwargs: Optional[Union[dict, List[dict]]] = None,
      filename: Optional[str] = None,
      render_distributions: bool = False,
      render_params: bool = False,
      render_deterministic: bool = False,
  ) -> "graphviz.Digraph":
      """
      Renders a model using `graphviz <https://graphviz.org>`_ .
  
      If ``filename`` is provided, this saves an image; otherwise this draws the
      graph. For example usage see the
      `model rendering tutorial <https://pyro.ai/examples/model_rendering.html>`_ .
  
      :param model: Model to render.
      :param model_args: Tuple of positional arguments to pass to the model, or
          list of tuples for semisupervised models.
      :param model_kwargs: Dict of keyword arguments to pass to the model, or
          list of dicts for semisupervised models.
      :param str filename: Name of file or path to file to save rendered model in.
      :param bool render_distributions: Whether to include RV distribution
          annotations (and param constraints) in the plot.
      :param bool render_params: Whether to show params inthe plot.
      :param bool render_deterministic: Whether to include deterministic sites.
      :returns: A model graph.
      :rtype: graphviz.Digraph
      """
      # Get model relations.
      if not isinstance(model_args, list) and not isinstance(
          model_kwargs, list
      ):
          relations = [
              get_model_relations(
                  model,
                  model_args,
                  model_kwargs,
                  include_deterministic=render_deterministic,
              )
          ]
      else:  # semisupervised
          if isinstance(model_args, list):
              if not isinstance(model_kwargs, list):
                  model_kwargs = [model_kwargs] * len(model_args)
          elif not isinstance(model_args, list):
              model_args = [model_args] * len(model_kwargs)
          assert len(model_args) == len(model_kwargs)
          relations = [
              get_model_relations(
                  model,
                  args,
                  kwargs,
                  include_deterministic=render_deterministic,
              )
              for args, kwargs in zip(model_args, model_kwargs)
          ]
  
      # Get graph specifications.
      graph_specs = [
          generate_graph_specification(r, render_params=render_params)
          for r in relations
      ]
      graph_spec = _deep_merge(graph_specs)
  
      # Render.
      graph = render_graph(
          graph_spec, render_distributions=render_distributions
      )
  
      if filename is not None:
          suffix = Path(filename).suffix[
              1:
          ]  # remove leading period from suffix
          filepath = os.path.splitext(filename)[0]
          graph.render(
              filepath, view=False, cleanup=True, format=suffix
          )
  
      return graph
  
imports: import itertools
  import os
  from collections import defaultdict
  from pathlib import Path
  from types import SimpleNamespace
  from typing import Callable, Collection, Dict, List, Optional, Union
  import torch
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.ops.provenance import ProvenanceTensor, detach_provenance, get_provenance
  from pyro.poutine.messenger import Messenger
  from pyro.poutine.util import site_is_subsample
label_desc: Use image node_id 9 for calling a global function with example usage: render_model(model, model_args, model_kwargs, filename, render_distributions, render_params, render_deterministic) and returns: graph