code: def range_input_producer(
      limit,
      num_epochs=None,
      shuffle=True,
      seed=None,
      capacity=32,
      shared_name=None,
      name=None,
  ):
      """Produces the integers from 0 to limit-1 in a queue.
  
      Note: if `num_epochs` is not `None`, this function creates local counter
      `epochs`. Use `local_variables_initializer()` to initialize local variables.
  
      Args:
        limit: An int32 scalar tensor.
        num_epochs: An integer (optional). If specified, `range_input_producer`
          produces each integer `num_epochs` times before generating an
          OutOfRange error. If not specified, `range_input_producer` can cycle
          through the integers an unlimited number of times.
        shuffle: Boolean. If true, the integers are randomly shuffled within each
          epoch.
        seed: An integer (optional). Seed used if shuffle == True.
        capacity: An integer. Sets the queue capacity.
        shared_name: (optional). If set, this queue will be shared under the given
          name across multiple sessions.
        name: A name for the operations (optional).
  
      Returns:
        A Queue with the output integers.  A `QueueRunner` for the Queue
        is added to the current `Graph`'s `QUEUE_RUNNER` collection.
  
      @compatibility(eager)
      Input pipelines based on Queues are not supported when eager execution is
      enabled. Please use the `tf.data` API to ingest data under eager execution.
      @end_compatibility
      """
      with ops.name_scope(name, "input_producer", [limit]) as name:
          range_tensor = math_ops.range(limit)
          return input_producer(
              range_tensor,
              [],
              num_epochs,
              shuffle,
              seed,
              capacity,
              shared_name,
              "fraction_of_%d_full" % capacity,
              name,
          )
  
imports: from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.layers import utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_assert
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import data_flow_ops
  from tensorflow.python.ops import io_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import variable_v1
  from tensorflow.python.summary import summary
  from tensorflow.python.training import queue_runner
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.compat import collections_abc
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 5 for calling a global function with example usage: range_input_producer(limit, num_epochs, shuffle, seed, capacity, shared_name, name) and returns: input_producer