class_docstr: A streaming Dataset executor.
  
  This implementation executes Dataset DAGs in a fully streamed way. It runs
  by setting up the operator topology, and then routing blocks through operators in
  a way that maximizes throughput under resource constraints.
class_docstr_tok: ["A","streaming","Dataset","executor",".","This","implementation","executes","Dataset","DAGs","in","a","fully","streamed","way",".","It","runs","by","setting","up","the","operator","topology",",","and","then","routing","blocks","through","operators","in","a","way","that","maximizes","throughput","under","resource","constraints","."]
docstr: Returns whether the user thread is blocked on topology execution.
docstr_tok: ["Returns","whether","the","user","thread","is","blocked","on","topology","execution","."]
code: def _consumer_idling(self) -> bool:
      """Returns whether the user thread is blocked on topology execution."""
      return len(self._output_node.outqueue) == 0
  
imports: import os
  import threading
  import time
  import uuid
  from typing import Dict, Iterator, List, Optional
  from ray.data._internal.dataset_logger import DatasetLogger
  from ray.data._internal.execution.autoscaling_requester import get_or_create_autoscaling_requester_actor
  from ray.data._internal.execution.backpressure_policy import BackpressurePolicy, get_backpressure_policies
  from ray.data._internal.execution.interfaces import ExecutionOptions, ExecutionResources, Executor, OutputIterator, PhysicalOperator, RefBundle
  from ray.data._internal.execution.operators.input_data_buffer import InputDataBuffer
  from ray.data._internal.execution.streaming_executor_state import DEFAULT_OBJECT_STORE_MEMORY_LIMIT_FRACTION, AutoscalingState, OpState, Topology, TopologyResourceUsage, build_streaming_topology, process_completed_tasks, select_operator_to_run, update_operator_states
  from ray.data._internal.progress_bar import ProgressBar
  from ray.data._internal.stats import DatasetStats, StatsManager
  from ray.data._internal.util import cluster_resources
  from ray.data.context import DataContext
label_desc: Use image node_id 9 for calling the StreamingExecutor obj's underlying member method code with example usage: obj._consumer_idling() and returns: unknown