class_docstr: This connector does 2 things:
  1. It filters data columns based on view_requirements for training and inference.
  2. It buffers the right amount of history for computing the sample batch for
     action computation.
  The output of this connector is AgentConnectorsOut, which basically is
  a tuple of 2 things:
  {
      "raw_dict": {"obs": ...}
      "sample_batch": SampleBatch
  }
  raw_dict, which contains raw data for the latest time slice,
  can be used to construct a complete episode by Sampler for training purpose.
  The "for_action" SampleBatch can be used to directly call the policy.
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code: def from_state(ctx: ConnectorContext, params: Any):
      return ViewRequirementAgentConnector(ctx)
  
file_path: ray/python/ray/rllib/connectors/agent/view_requirement.py
imports: from collections import defaultdict
  from typing import Any
  from ray.rllib.connectors.connector import AgentConnector, ConnectorContext
  from ray.rllib.connectors.registry import register_connector
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.typing import AgentConnectorDataType, AgentConnectorsOutput
  from ray.util.annotations import PublicAPI
  from ray.rllib.evaluation.collectors.agent_collector import AgentCollector
label_desc: Use image node_id 5 for calling the ViewRequirementAgentConnector obj's underlying member method code with example usage: obj.from_state(ctx, params) and returns: ViewRequirementAgentConnector