class_docstr: Defines a configuration class from which a DreamerV3 can be built.
  
  .. testcode::
  
      from ray.rllib.algorithms.dreamerv3 import DreamerV3Config
      config = (
          DreamerV3Config()
          .environment("CartPole-v1")
          .training(
              model_size="XS",
              training_ratio=1,
              # TODO
              model={
                  "batch_size_B": 1,
                  "batch_length_T": 1,
                  "horizon_H": 1,
                  "gamma": 0.997,
                  "model_size": "XS",
              },
          )
      )
  
      config = config.resources(num_learner_workers=0)
      # Build a Algorithm object from the config and run 1 training iteration.
      algo = config.build()
      # algo.train()
      del algo
  
  .. testoutput::
      :hide:
  
      ...
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docstr: Sets the reporting related configuration.
  
  Args:
      report_individual_batch_item_stats: Whether to include loss and other stats
          per individual timestep inside the training batch in the result dict
          returned by `training_step()`. If True, besides the `CRITIC_L_total`,
          the individual critic loss values per batch row and time axis step
          in the train batch (CRITIC_L_total_B_T) will also be part of the
          results.
      report_dream_data:  Whether to include the dreamed trajectory data in the
          result dict returned by `training_step()`. If True, however, will
          slice each reported item in the dream data down to the shape.
          (H, B, t=0, ...), where H is the horizon and B is the batch size. The
          original time axis will only be represented by the first timestep
          to not make this data too large to handle.
      report_images_and_videos: Whether to include any image/video data in the
          result dict returned by `training_step()`.
      **kwargs:
  
  Returns:
      This updated AlgorithmConfig object.
code: def reporting(
      self,
      *,
      report_individual_batch_item_stats: Optional[bool] = NotProvided,
      report_dream_data: Optional[bool] = NotProvided,
      report_images_and_videos: Optional[bool] = NotProvided,
      **kwargs,
  ):
      """Sets the reporting related configuration.
  
      Args:
          report_individual_batch_item_stats: Whether to include loss and other stats
              per individual timestep inside the training batch in the result dict
              returned by `training_step()`. If True, besides the `CRITIC_L_total`,
              the individual critic loss values per batch row and time axis step
              in the train batch (CRITIC_L_total_B_T) will also be part of the
              results.
          report_dream_data:  Whether to include the dreamed trajectory data in the
              result dict returned by `training_step()`. If True, however, will
              slice each reported item in the dream data down to the shape.
              (H, B, t=0, ...), where H is the horizon and B is the batch size. The
              original time axis will only be represented by the first timestep
              to not make this data too large to handle.
          report_images_and_videos: Whether to include any image/video data in the
              result dict returned by `training_step()`.
          **kwargs:
  
      Returns:
          This updated AlgorithmConfig object.
      """
      super().reporting(**kwargs)
  
      if report_individual_batch_item_stats is not NotProvided:
          self.report_individual_batch_item_stats = (
              report_individual_batch_item_stats
          )
      if report_dream_data is not NotProvided:
          self.report_dream_data = report_dream_data
      if report_images_and_videos is not NotProvided:
          self.report_images_and_videos = report_images_and_videos
  
      return self
  
imports: import copy
  import gc
  import logging
  import tree
  from typing import Any, Dict, List, Optional, Union
  import gymnasium
  import numpy
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.dreamerv3.dreamerv3_catalog import DreamerV3Catalog
  from ray.rllib.algorithms.dreamerv3.utils import do_symlog_obs
  from ray.rllib.algorithms.dreamerv3.utils.env_runner import DreamerV3EnvRunner
  from ray.rllib.algorithms.dreamerv3.utils.summaries import report_predicted_vs_sampled_obs, report_sampling_and_replay_buffer
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.models.catalog import MODEL_DEFAULTS
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch
  from ray.rllib.utils import deep_update
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.numpy import one_hot
  from ray.rllib.utils.metrics import ALL_MODULES, GARBAGE_COLLECTION_TIMER, LEARN_ON_BATCH_TIMER, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, NUM_GRAD_UPDATES_LIFETIME, NUM_SYNCH_WORKER_WEIGHTS, NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS, SAMPLE_TIMER, SYNCH_WORKER_WEIGHTS_TIMER
  from ray.rllib.utils.replay_buffers.episode_replay_buffer import EpisodeReplayBuffer
  from ray.rllib.utils.typing import LearningRateOrSchedule, ResultDict
label_desc: Use image node_id 5 for calling the DreamerV3Config obj's underlying member method code with example usage: obj.reporting() and returns: self