code: def _tf_tensors_nonngram(
      reader, shuffling_queue_capacity, min_after_dequeue
  ):
      """A tensorflow data adapter for non ngrams. Return value is a named tuple with tensorflow tensors supplying
      the data directly into a Tensoflow graph. See `tf_tensor` documentation for input/output arguments meaning.
      """
  
      # TODO(yevgeni): implement a mechanism for signaling that we have no more data
      def dequeue_sample_impl(x):
          next_sample = next(reader)
          # Decimal is not supported by TF. int8,16,32,64 scalars are all returned as python native int type
          # (casted to 64 bit by tensorflow). sanitize_field_tf_types will explicitly convert all values
          # to explicit numpy types making it compatible with return values expected by Tensorflow
          return _sanitize_field_tf_types(next_sample)
  
      # fields_as_list is a list with tensors matching the order of the values in the schema. named-tuple semantics is
      # not preserved across tf.py_func call boundary.
      fields_as_list = tf.py_func(
          dequeue_sample_impl,
          [tf.constant(1)],
          _schema_to_tf_dtypes(reader.schema),
      )
  
      if shuffling_queue_capacity > 0:
          # Pass py_func output via shuffling queue if requested.
          fields_as_list = _shuffling_queue(
              shuffling_queue_capacity,
              min_after_dequeue,
              _schema_to_tf_dtypes(reader.schema),
              fields_as_list,
          )
  
      # Going via `make_namedtuple_tf` is a little wasteful, since we are converting directly to dict. However, this
      # spares the need to implement a function similar to make_namedtuple_tf that returns dict instead of a named tuple
      fields_as_dict = reader.schema.make_namedtuple_tf(
          *fields_as_list
      )._asdict()
  
      # Force all static shapes to be set in the returned value based on the unischema
      _set_shape(reader.schema, fields_as_dict, reader.batched_output)
  
      # Make a row tensor into a nice named tuple
      return reader.schema.make_namedtuple_tf(**fields_as_dict)
  
imports: import datetime
  import sys
  import warnings
  from calendar import timegm
  from collections import OrderedDict, namedtuple
  from decimal import Decimal
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 10 for calling a global function with example usage: _tf_tensors_nonngram(reader, shuffling_queue_capacity, min_after_dequeue) and returns: reader, _sanitize_field_tf_types