class_docstr: Defines an abstract neural network model for use with RLlib.
  
  Custom models should extend either TFModelV2 or TorchModelV2 instead of
  this class directly.
  
  Data flow:
      obs -> forward() -> model_out
          \-> value_function() -> V(s)
class_docstr_tok: ["Defines","an","abstract","neural","network","model","for","use","with","RLlib",".","Custom","models","should","extend","either","TFModelV2","or","TorchModelV2","instead","of","this","class","directly",".","Data","flow",":","obs","-",">","forward","(",")","-",">","model_out","\\-",">","value_function","(",")","-",">","V","(","s",")"]
docstr: Override to return custom metrics from your model.
  
  The stats will be reported as part of the learner stats, i.e.,
  info.learner.[policy_id, e.g. "default_policy"].model.key1=metric1
  
  Returns:
      The custom metrics for this model.
docstr_tok: ["Override","to","return","custom","metrics","from","your","model",".","The","stats","will","be","reported","as","part","of","the","learner","stats",",","i.e.",",","info.learner",".","[","policy_id",",","e.g",".","``","default_policy","''","]",".model.key1=metric1","Returns",":","The","custom","metrics","for","this","model","."]
code: def metrics(self) -> Dict[str, TensorType]:
      """Override to return custom metrics from your model.
  
      The stats will be reported as part of the learner stats, i.e.,
      info.learner.[policy_id, e.g. "default_policy"].model.key1=metric1
  
      Returns:
          The custom metrics for this model.
      """
      return {}
  
imports: from collections import OrderedDict
  import contextlib
  import gymnasium
  from gymnasium.spaces import Space
  import numpy
  from typing import Dict, List, Any, Union
  from ray.rllib.models.preprocessors import get_preprocessor, RepeatedValuesPreprocessor
  from ray.rllib.models.repeated_values import RepeatedValues
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils import NullContextManager
  from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI
  from ray.rllib.utils.deprecation import Deprecated
  from ray.rllib.utils.framework import try_import_tf, try_import_torch, TensorType
  from ray.rllib.utils.spaces.repeated import Repeated
  from ray.rllib.utils.typing import ModelConfigDict, ModelInputDict, TensorStructType
label_desc: Use image node_id 6 for calling the ModelV2 obj's underlying member method code with example usage: obj.metrics() and returns: dict