def dequeue(self):
    """Dequeues a batch of tensor_dict from the BatchQueue.

    TODO: use allow_smaller_final_batch to allow running over the whole eval set

    Returns:
      A list of tensor_dicts of the requested batch_size.
    """
    batched_tensors = self._queue.dequeue()
    # Separate input tensors from tensors containing their runtime shapes.
    tensors = {}
    shapes = {}
    for key, batched_tensor in batched_tensors.items():
      unbatched_tensor_list = tf.unstack(batched_tensor)
      for i, unbatched_tensor in enumerate(unbatched_tensor_list):
        if rt_shape_str in key:
          shapes[(key[:-len(rt_shape_str)], i)] = unbatched_tensor
        else:
          tensors[(key, i)] = unbatched_tensor

    # Undo that padding using shapes and create a list of size `batch_size` that
    # contains tensor dictionaries.
    tensor_dict_list = []
    batch_size = self._batch_size
    for batch_id in range(batch_size):
      tensor_dict = {}
      for key in self._static_shapes:
        tensor_dict[key] = tf.slice(tensors[(key, batch_id)],
                                    tf.zeros_like(shapes[(key, batch_id)]),
                                    shapes[(key, batch_id)])
        tensor_dict[key].set_shape(self._static_shapes[key])
      tensor_dict_list.append(tensor_dict)

    return tensor_dict_list
Dequeues a batch of tensor_dict from the BatchQueue.

    TODO: use allow_smaller_final_batch to allow running over the whole eval set

    Returns:
      A list of tensor_dicts of the requested batch_size.
    
inference_api/src/main/object_detection/core/batcher.py