code: def evaluate(
      summary_dir: Text,
      environment_name: Text,
      policy: py_tf_eager_policy.PyTFEagerPolicyBase,
      variable_container: reverb_variable_container.ReverbVariableContainer,
      suite_load_fn: Callable[
          [Text], py_environment.PyEnvironment
      ] = suite_mujoco.load,
      additional_metrics: Optional[
          Iterable[py_metric.PyStepMetric]
      ] = None,
      is_running: Optional[Callable[[], bool]] = None,
      eval_interval: int = 1000,
      eval_episodes: int = 1,
      # TODO(b/178225158): Deprecate in favor of the reporting libray when ready.
      return_reporting_fn: Optional[
          Callable[[int, float], None]
      ] = None,
  ) -> None:
      """Evaluates a policy iteratively fetching weights from variable container.
  
      Args:
        summary_dir: Directory which is used to store the summaries.
        environment_name: Name of the environment used to evaluate the policy.
        policy: The policy being evaluated. The weights of this policy are fetched
          from the variable container periodically.
        variable_container: Provides weights for the policy.
        suite_load_fn: Function that loads the environment (by calling it with the
          name of the environment) from a particular suite.
        additional_metrics: Optional collection of metrics that are computed as well
          during the evaluation. By default (`None`) it is empty.
        is_running: Optional callable which controls the running of the main
          evaluation loop (including fetching weights from the variable container
          and running the eval actor periodically). By default (`None`) this is a
          callable always returning `True` resulting in an infinite evaluation loop.
        eval_interval: If set, eval is done at the given step interval or as close
          as possible based on polling.
        eval_episodes: Number of episodes to eval.
        return_reporting_fn: Optional callback function of the form `fn(train_step,
          average_return)` which reports the average return to a custom destination.
      """
      additional_metrics = additional_metrics or []
      is_running = is_running or (lambda: True)
      environment = suite_load_fn(environment_name)
  
      # Create the variable container.
      train_step = train_utils.create_train_step()
      variables = {
          reverb_variable_container.POLICY_KEY: policy.variables(),
          reverb_variable_container.TRAIN_STEP_KEY: train_step,
      }
      variable_container.update(variables)
      prev_train_step_value = train_step.numpy()
  
      # Create the evaluator actor.
      metrics = actor.collect_metrics(buffer_size=eval_episodes)
  
      if return_reporting_fn:
          for m in metrics:
              if isinstance(m, py_metrics.AverageReturnMetric):
                  average_return_metric = m
                  break
  
      eval_actor = actor.Actor(
          environment,
          policy,
          train_step,
          episodes_per_run=eval_episodes,
          summary_dir=summary_dir,
          summary_interval=eval_interval,
          metrics=metrics + additional_metrics,
          name="eval_actor",
      )
  
      # Run the experience evaluation loop.
      last_eval_step = 0
      while is_running():
          # Eval every step if no `eval_interval` is set, or if on the first step, or
          # if the step is equal or greater than `last_eval_step` + `eval_interval`.
          # It is very possible when logging a specific interval that the steps evaled
          # will not be exact, e.g. 1001 and then 2003 vs. 1000 and then 2000.
          if (
              train_step.numpy() == 0
              or train_step.numpy() >= eval_interval + last_eval_step
          ):
              logging.info(
                  "Evaluating using greedy policy at step: %d",
                  train_step.numpy(),
              )
              eval_actor.run()
              last_eval_step = train_step.numpy()
  
          def is_train_step_the_same_or_behind():
              # Checks if the `train_step` received from variable conainer is the same
              # (or behind) the latest evaluated train step (`prev_train_step_value`).
              variable_container.update(variables)
              return train_step.numpy() <= prev_train_step_value
  
          train_utils.wait_for_predicate(
              wait_predicate_fn=is_train_step_the_same_or_behind
          )
          prev_train_step_value = train_step.numpy()
  
          # Optionally report the average return metric via a callback.
          if return_reporting_fn:
              return_reporting_fn(
                  train_step.numpy(), average_return_metric.result()
              )
  
imports: import functools
  import os
  from typing import Callable, Iterable, Optional, Sequence, Text
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  from tf_agents.environments import py_environment
  from tf_agents.environments import suite_mujoco
  from tf_agents.experimental.distributed import reverb_variable_container
  from tf_agents.metrics import py_metric
  from tf_agents.metrics import py_metrics
  from tf_agents.policies import greedy_policy
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.system import system_multiprocessing
  from tf_agents.train import actor
  from tf_agents.train import learner
  from tf_agents.train.utils import train_utils
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(summary_dir, environment_name, policy, variable_container, suite_load_fn, additional_metrics, is_running, eval_interval, eval_episodes, return_reporting_fn) and returns: unknown