class_docstr: Collects already postprocessed (single agent) samples for one policy.
  
  Samples come in through already postprocessed SampleBatches, which
  contain single episode/trajectory data for a single agent and are then
  appended to this policy's buffers.
docstr: Adds a postprocessed SampleBatch (single agent) to our buffers.
  
  Args:
      batch: An individual agent's (one trajectory)
          SampleBatch to be added to the Policy's buffers.
      view_requirements: The view
          requirements for the policy. This is so we know, whether a
          view-column needs to be copied at all (not needed for
          training).
code: def add_postprocessed_batch_for_training(
      self, batch: SampleBatch, view_requirements: ViewRequirementsDict
  ) -> None:
      """Adds a postprocessed SampleBatch (single agent) to our buffers.
  
      Args:
          batch: An individual agent's (one trajectory)
              SampleBatch to be added to the Policy's buffers.
          view_requirements: The view
              requirements for the policy. This is so we know, whether a
              view-column needs to be copied at all (not needed for
              training).
      """
      # Add the agent's trajectory length to our count.
      self.agent_steps += batch.count
      # And remove columns not needed for training.
      for view_col, view_req in view_requirements.items():
          if view_col in batch and not view_req.used_for_training:
              del batch[view_col]
      self.batches.append(batch)
  
imports: import collections
  from gymnasium.spaces import Space
  import logging
  import numpy
  import tree
  from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union
  from ray.rllib.env.base_env import _DUMMY_AGENT_ID
  from ray.rllib.evaluation.collectors.sample_collector import SampleCollector
  from ray.rllib.evaluation.collectors.agent_collector import AgentCollector
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_map import PolicyMap
  from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch, concat_samples
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.debug import summarize
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.spaces.space_utils import get_dummy_batch_for_space
  from ray.rllib.utils.typing import AgentID, EpisodeID, EnvID, PolicyID, TensorType, ViewRequirementsDict
  from ray.util.debug import log_once
label_desc: Use image node_id 2 for calling the _PolicyCollector obj's underlying member method code with example usage: obj.add_postprocessed_batch_for_training(batch, view_requirements) without return types