code: def aggregating_observers(self):
      return {
          name: IntervalAggregatingObserver(
              self.report_interval, aggregator
          )
          for name, aggregator in itertools.chain(
              [
                  (
                      "mse_loss_per_batch",
                      agg.MeanAggregator("mse_loss"),
                  ),
                  (
                      "step_entropy_loss_per_batch",
                      agg.MeanAggregator("step_entropy_loss"),
                  ),
                  (
                      "q_values_per_batch",
                      agg.FunctionsByActionAggregator(
                          "q_values",
                          self.action_names,
                          {"mean": torch.mean},
                      ),
                  ),
                  (
                      "eval_mse_loss_per_batch",
                      agg.MeanAggregator("eval_mse_loss"),
                  ),
                  (
                      "eval_step_entropy_loss_per_batch",
                      agg.MeanAggregator("eval_step_entropy_loss"),
                  ),
                  (
                      "eval_q_values_per_batch",
                      agg.FunctionsByActionAggregator(
                          "eval_q_values",
                          self.action_names,
                          {"mean": torch.mean},
                      ),
                  ),
                  (
                      "eval_action_distribution_per_batch",
                      agg.FunctionsByActionAggregator(
                          "eval_action_distribution",
                          self.action_names,
                          {"mean": torch.mean},
                      ),
                  ),
              ],
              [
                  (
                      f"{key}_tb",
                      agg.TensorBoardHistogramAndMeanAggregator(
                          key, log_key
                      ),
                  )
                  for key, log_key in [
                      ("mse_loss", "mse_loss"),
                      ("step_entropy_loss", "step_entropy_loss"),
                      ("eval_mse_loss", "eval_mse_loss"),
                      (
                          "eval_step_entropy_loss",
                          "eval_step_entropy_loss",
                      ),
                  ]
              ],
              [
                  (
                      f"{key}_tb",
                      agg.TensorBoardActionHistogramAndMeanAggregator(
                          key, category, title, self.action_names
                      ),
                  )
                  for key, category, title in [
                      ("q_values", "q_values", "training"),
                      ("eval_q_values", "q_values", "eval"),
                      (
                          "eval_action_distribution",
                          "action_distribution",
                          "eval",
                      ),
                  ]
              ],
          )
      }
  
file_path: ReAgent/reagent/reporting/seq2reward_reporter.py
imports: import itertools
  import logging
  from typing import List
  import torch
  from reagent.core import aggregators
  from reagent.core.observers import IntervalAggregatingObserver
  from reagent.reporting.reporter_base import ReporterBase
  from reagent.workflow.training_reports import Seq2RewardTrainingReport
label_desc: Use image node_id 3 for calling the Seq2RewardReporter obj's underlying member method code with example usage: obj.aggregating_observers() and returns: unknown