code: def grad_stats_fn(
      self, train_batch: SampleBatch, grads: ModelGradients
  ) -> Dict[str, TensorType]:
      # We have support for more than one loss (list of lists of grads).
      if self.config.get("_tf_policy_handles_more_than_one_loss"):
          grad_gnorm = [tf.linalg.global_norm(g) for g in grads]
      # Old case: We have a single list of grads (only one loss term and
      # optimizer).
      else:
          grad_gnorm = tf.linalg.global_norm(grads)
  
      return {
          "grad_gnorm": grad_gnorm,
      }
  
imports: import logging
  from typing import Dict, List
  import numpy
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.eager_tf_policy import EagerTFPolicy
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.policy import Policy, PolicyState
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_policy import TFPolicy
  from ray.rllib.utils.annotations import DeveloperAPI, override
  from ray.rllib.utils.framework import get_variable, try_import_tf
  from ray.rllib.utils.schedules import PiecewiseSchedule
  from ray.rllib.utils.tf_utils import make_tf_callable
  from ray.rllib.utils.typing import AlgorithmConfigDict, LocalOptimizer, ModelGradients, TensorType
label_desc: Use image node_id 2 for calling the GradStatsMixin obj's underlying member method code with example usage: obj.grad_stats_fn(train_batch, grads) and returns: dict