Query: tf idf during single testing python

Relevant code:
def main(argv=None):
  """Run a Tensorflow model on the Iris dataset."""
  args = parse_arguments(sys.argv if argv is None else argv)

  tf.logging.set_verbosity(tf.logging.INFO)
  learn_runner.run(
      experiment_fn=get_experiment_fn(args),
      output_dir=args.job_dir)

Similar but not relevant code:
def _config_session():
        """
        Configure session for particular device

        Returns:
            tensorflow.Session
        """
        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True
        config.gpu_options.visible_device_list = '0'
        return tf.Session(config=config)

Similar but not relevant code:
def run_std_server(self):
    """Starts a TensorFlow server and joins the serving thread.

    Typically used for parameter servers.

    Raises:
      ValueError: if not enough information is available in the estimator's
        config to create a server.
    """
    config = tf.estimator.RunConfig()
    server = tf.train.Server(
        config.cluster_spec,
        job_name=config.task_type,
        task_index=config.task_id,
        protocol=config.protocol)
    server.join()

Similar but not relevant code:
def get_trace_id_from_flask():
    """Get trace_id from flask request headers.

    :rtype: str
    :returns: TraceID in HTTP request headers.
    """
    if flask is None or not flask.request:
        return None

    header = flask.request.headers.get(_FLASK_TRACE_HEADER)

    if header is None:
        return None

    trace_id = header.split("/", 1)[0]

    return trace_id

Similar but not relevant code:
def iparallel_progbar(mapper, iterable, nprocs=None, starmap=False, flatmap=False, shuffle=False,
                      verbose=True, verbose_flatmap=None, max_cache=-1, **kwargs):
    """Performs a parallel mapping of the given iterable, reporting a progress bar as values get returned. Yields
    objects as soon as they're computed, but does not guarantee that they'll be in the correct order.

    :param mapper: The mapping function to apply to elements of the iterable
    :param iterable: The iterable to map
    :param nprocs: The number of processes (defaults to the number of cpu's)
    :param starmap: If true, the iterable is expected to contain tuples and the mapper function gets each element of a
        tuple as an argument
    :param flatmap: If true, flatten out the returned values if the mapper function returns a list of objects
    :param shuffle: If true, randomly sort the elements before processing them. This might help provide more uniform
        runtimes if processing different objects takes different amounts of time.
    :param verbose: Whether or not to print the progress bar
    :param verbose_flatmap: If performing a flatmap, whether or not to report each object as it's returned
    :param max_cache: Maximum number of mapped objects to permit in the queue at once
    :param kwargs: Any other keyword arguments to pass to the progress bar (see ``progbar``)
    :return: A list of the returned objects, in whatever order they're done being computed
    """

    results = _parallel_progbar_launch(mapper, iterable, nprocs, starmap, flatmap, shuffle, verbose,
                                       verbose_flatmap, max_cache, **kwargs)
    return (x for i, x in results)

Similar but not relevant code:
def partial_fit(self, X, y=None, classes=None, **fit_params):
        """Fit the module.

        If the module is initialized, it is not re-initialized, which
        means that this method should be used if you want to continue
        training a model (warm start).

        Parameters
        ----------
        X : input data, compatible with skorch.dataset.Dataset
          By default, you should be able to pass:

            * numpy arrays
            * torch tensors
            * pandas DataFrame or Series
            * scipy sparse CSR matrices
            * a dictionary of the former three
            * a list/tuple of the former three
            * a Dataset

          If this doesn't work with your data, you have to pass a
          ``Dataset`` that can deal with the data.

        y : target data, compatible with skorch.dataset.Dataset
          The same data types as for ``X`` are supported. If your X is
          a Dataset that contains the target, ``y`` may be set to
          None.

        classes : array, sahpe (n_classes,)
          Solely for sklearn compatibility, currently unused.

        **fit_params : dict
          Additional parameters passed to the ``forward`` method of
          the module and to the ``self.train_split`` call.

        """
        if not self.initialized_:
            self.initialize()

        self.notify('on_train_begin', X=X, y=y)
        try:
            self.fit_loop(X, y, **fit_params)
        except KeyboardInterrupt:
            pass
        self.notify('on_train_end', X=X, y=y)
        return self