code: def __init__(
      self,
      net,
      input_blob_names,
      batch_size,
      device_option,
      namescope,
      input_source_name,
      queue,
      metrics,
      dont_rebatch,
      batch_columns,
      timeout=600,
  ):
      self._counter = 0
      self._input_blob_names = input_blob_names
      self._batch_size = batch_size
      self._internal_queue = queue
      self._queues = []
      self._device_option = device_option
      self._namescope = namescope
      self._timeout = timeout
      self._input_source_name = input_source_name
      self._c2_queue_capacity = 4
      self._create_caffe2_queues(net)
      self._create_caffe2_ops(net)
      self._inputs = 0
      self._prev_seconds = 0
      self._last_warning = time.time()
      self._dont_rebatch = dont_rebatch
      self._init_scratch()
      self._metrics = metrics
  
      if batch_columns is None:
          batch_columns = [0 for _ in input_blob_names]
      self._batch_columns = batch_columns
  
imports: import queue
  from itertools import chain
  import logging
  import threading
  import numpy
  import time
  from caffe2.python import workspace, core, scope, utils
  from caffe2.proto import caffe2_pb2
  from caffe2.python.parallel_workers import Metrics, State, WorkerCoordinator, GlobalWorkerCoordinator, Worker, run_worker
label_desc: Use image node_id 1 to create a new BatchFeeder object from inherited base classes: State with example: obj = BatchFeeder(net, input_blob_names, batch_size, device_option, namescope, input_source_name, queue, metrics, dont_rebatch, batch_columns, timeout)