code: def _as_original_type(original_tensors, tensor_list):
      if isinstance(original_tensors, dict):
          if len(original_tensors) == 1:
              # tensor_list is bogusly returned as a single tensor if only one tensor
              # was enqueued.  Make it a list again.  See b/28117485.
              tensor_list = [tensor_list]
          return {
              k: tensor_list[i]
              for i, k in enumerate(sorted(original_tensors, key=str))
          }
      else:
          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 10 for calling a global function with example usage: _as_original_type(original_tensors, tensor_list) and returns: unknown, tensor_list