parameters: (output_dict, feed_dicts, restore_checkpoint_path=None)
docstring: Run `output_dict` tensors with each input in `feed_dicts`.
  
    If `restore_checkpoint_path` is supplied, restore from checkpoint. Otherwise,
    init all variables.
  
    Args:
      output_dict: A `dict` mapping string names to `Tensor` objects to run.
        Tensors must all be from the same graph.
      feed_dicts: Iterable of `dict` objects of input values to feed.
      restore_checkpoint_path: A string containing the path to a checkpoint to
        restore.
  
    Yields:
      A sequence of dicts of values read from `output_dict` tensors, one item
      yielded for each item in `feed_dicts`. Keys are the same as `output_dict`,
      values are the results read from the corresponding `Tensor` in
      `output_dict`.
  
    Raises:
      ValueError: if `output_dict` or `feed_dicts` is None or empty.
docstring_summary: Run `output_dict` tensors with each input in `feed_dicts`.
docstring_tokens: Run output_dict tensors with each input in feed_dicts .
function: def run_feeds_iter(output_dict, feed_dicts, restore_checkpoint_path=None):
    """Run `output_dict` tensors with each input in `feed_dicts`.
  
    If `restore_checkpoint_path` is supplied, restore from checkpoint. Otherwise,
    init all variables.
  
    Args:
      output_dict: A `dict` mapping string names to `Tensor` objects to run.
        Tensors must all be from the same graph.
      feed_dicts: Iterable of `dict` objects of input values to feed.
      restore_checkpoint_path: A string containing the path to a checkpoint to
        restore.
  
    Yields:
      A sequence of dicts of values read from `output_dict` tensors, one item
      yielded for each item in `feed_dicts`. Keys are the same as `output_dict`,
      values are the results read from the corresponding `Tensor` in
      `output_dict`.
  
    Raises:
      ValueError: if `output_dict` or `feed_dicts` is None or empty.
    """
    if not output_dict:
      raise ValueError('output_dict is invalid: %s.' % output_dict)
    if not feed_dicts:
      raise ValueError('feed_dicts is invalid: %s.' % feed_dicts)
  
    graph = contrib_ops.get_graph_from_inputs(output_dict.values())
  
    with graph.as_default() as g:
      with tf_session.Session('') as session:
        if restore_checkpoint_path:
          _restore_from_checkpoint(session, g, restore_checkpoint_path)
        else:
          session.run(variables.initialize_all_variables())
        session.run(variables.initialize_local_variables())
        session.run(data_flow_ops.initialize_all_tables())
        coord = coordinator.Coordinator()
        threads = None
        try:
          threads = queue_runner.start_queue_runners(session, coord=coord)
          for f in feed_dicts:
            yield session.run(output_dict, f)
        finally:
          coord.request_stop()
          if threads:
            coord.join(threads, stop_grace_period_secs=120)
function_tokens: def run_feeds_iter ( output_dict , feed_dicts , restore_checkpoint_path = None ) : if not output_dict : raise ValueError ( 'output_dict is invalid: %s.' % output_dict ) if not feed_dicts : raise ValueError ( 'feed_dicts is invalid: %s.' % feed_dicts ) graph = contrib_ops . get_graph_from_inputs ( output_dict . values ( ) ) with graph . as_default ( ) as g : with tf_session . Session ( '' ) as session : if restore_checkpoint_path : _restore_from_checkpoint ( session , g , restore_checkpoint_path ) else : session . run ( variables . initialize_all_variables ( ) ) session . run ( variables . initialize_local_variables ( ) ) session . run ( data_flow_ops . initialize_all_tables ( ) ) coord = coordinator . Coordinator ( ) threads = None try : threads = queue_runner . start_queue_runners ( session , coord = coord ) for f in feed_dicts : yield session . run ( output_dict , f ) finally : coord . request_stop ( ) if threads : coord . join ( threads , stop_grace_period_secs = 120 )