class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  This class is intended to be used and extended by sub-classing.
docstr: Constructs loss computation graph for this TF1 policy.
  
  Args:
      model: The Model to calculate the loss for.
      dist_class: The action distr. class.
      train_batch: The training data.
  
  Returns:
      A single loss tensor or a list of loss tensors.
docstr_tok: ["Constructs","loss","computation","graph","for","this","TF1","policy",".","Args",":","model",":","The","Model","to","calculate","the","loss","for",".","dist_class",":","The","action","distr",".","class",".","train_batch",":","The","training","data",".","Returns",":","A","single","loss","tensor","or","a","list","of","loss","tensors","."]
code: def loss(
      self,
      model: Union[ModelV2, "tf.keras.Model"],
      dist_class: Type[TFActionDistribution],
      train_batch: SampleBatch,
  ) -> Union[TensorType, List[TensorType]]:
      """Constructs loss computation graph for this TF1 policy.
  
      Args:
          model: The Model to calculate the loss for.
          dist_class: The action distr. class.
          train_batch: The training data.
  
      Returns:
          A single loss tensor or a list of loss tensors.
      """
      raise NotImplementedError
  
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 4 for calling the DynamicTFPolicyV2 obj's underlying member method code with example usage: obj.loss(model, dist_class, train_batch) without return types