class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  This class is intended to be used and extended by sub-classing.
docstr: Gradients computing function (from loss tensor, using local optimizer).
  
  Args:
      optimizer: The tf (local) optimizer object to
          calculate the gradients with.
      grads: The gradient tensor to be applied.
  
  Returns:
      "tf.Operation": TF operation that applies supplied gradients.
docstr_tok: ["Gradients","computing","function","(","from","loss","tensor",",","using","local","optimizer",")",".","Args",":","optimizer",":","The","tf","(","local",")","optimizer","object","to","calculate","the","gradients","with",".","grads",":","The","gradient","tensor","to","be","applied",".","Returns",":","``","tf.Operation","''",":","TF","operation","that","applies","supplied","gradients","."]
code: def apply_gradients_fn(
      self,
      optimizer: "tf.keras.optimizers.Optimizer",
      grads: ModelGradients,
  ) -> "tf.Operation":
      """Gradients computing function (from loss tensor, using local optimizer).
  
      Args:
          optimizer: The tf (local) optimizer object to
              calculate the gradients with.
          grads: The gradient tensor to be applied.
  
      Returns:
          "tf.Operation": TF operation that applies supplied gradients.
      """
      return None
  
imports: from collections import OrderedDict
  import gymnasium
  import logging
  import re
  import tree
  from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Type, Union
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.policy.dynamic_tf_policy import TFMultiGPUTowerStack
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_policy import TFPolicy
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils import force_list
  from ray.rllib.utils.annotations import DeveloperAPI, OverrideToImplementCustomLogic, OverrideToImplementCustomLogic_CallToSuperRecommended, is_overridden, override
  from ray.rllib.utils.debug import summarize
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.metrics import DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY, NUM_GRAD_UPDATES_LIFETIME
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.spaces.space_utils import get_dummy_batch_for_space
  from ray.rllib.utils.tf_utils import get_placeholder
  from ray.rllib.utils.typing import AlgorithmConfigDict, LocalOptimizer, ModelGradients, TensorType
  from ray.util.debug import log_once
label_desc: Use image node_id 9 for calling the DynamicTFPolicyV2 obj's underlying member method code with example usage: obj.apply_gradients_fn(optimizer, grads) and returns: None