class_docstr: VTrace version of gradient computation logic.
class_docstr_tok: ["VTrace","version","of","gradient","computation","logic","."]
code: def compute_gradients_fn(
      self, optimizer: LocalOptimizer, loss: TensorType
  ) -> ModelGradients:
      # Supporting more than one loss/optimizer.
      if self.config.get("_enable_new_api_stack", False):
          # In order to access the variables for rl modules, we need to
          # use the underlying keras api model.trainable_variables.
          trainable_variables = self.model.trainable_variables
      else:
          trainable_variables = self.model.trainable_variables()
      if self.config["_tf_policy_handles_more_than_one_loss"]:
          optimizers = force_list(optimizer)
          losses = force_list(loss)
          assert len(optimizers) == len(losses)
          clipped_grads_and_vars = []
          for optim, loss_ in zip(optimizers, losses):
              grads_and_vars = optim.compute_gradients(
                  loss_, trainable_variables
              )
              clipped_g_and_v = []
              for g, v in grads_and_vars:
                  if g is not None:
                      clipped_g, _ = tf.clip_by_global_norm(
                          [g], self.config["grad_clip"]
                      )
                      clipped_g_and_v.append((clipped_g[0], v))
              clipped_grads_and_vars.append(clipped_g_and_v)
  
          self.grads = [
              g
              for g_and_v in clipped_grads_and_vars
              for (g, v) in g_and_v
          ]
      # Only one optimizer and and loss term.
      else:
          grads_and_vars = optimizer.compute_gradients(
              loss, self.model.trainable_variables()
          )
          grads = [g for (g, v) in grads_and_vars]
          self.grads, _ = tf.clip_by_global_norm(
              grads, self.config["grad_clip"]
          )
          clipped_grads_and_vars = list(
              zip(self.grads, trainable_variables)
          )
  
      return clipped_grads_and_vars
  
imports: import numpy
  import logging
  import gymnasium
  from typing import Dict, List, Optional, Type, Union
  from ray.rllib.algorithms.impala import vtrace_tf
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.postprocessing import compute_bootstrap_value
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import Categorical, TFActionDistribution
  from ray.rllib.policy.dynamic_tf_policy_v2 import DynamicTFPolicyV2
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LearningRateSchedule, EntropyCoeffSchedule
  from ray.rllib.utils import force_list
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.tf_utils import explained_variance
  from ray.rllib.policy.tf_mixins import GradStatsMixin, ValueNetworkMixin
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType, TFPolicyV2Type
label_desc: Use image node_id 2 for calling the VTraceClipGradients obj's underlying member method code with example usage: obj.compute_gradients_fn(optimizer, loss) and returns: clipped_grads_and_vars