code: def _select_which_to_enqueue(tensor_list, keep_input):
      """Select which examples to enqueue based on vector `keep_input`."""
      select_i = math_ops.cast(keep_input, dtypes.int32)
      tensor_list = [
          data_flow_ops.dynamic_partition(
              x, select_i, num_partitions=2
          )[1]
          for x in tensor_list
      ]
      return tensor_list
  
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 20 for calling a global function with example usage: _select_which_to_enqueue(tensor_list, keep_input) and returns: tensor_list