class_docstr: Base class for Detection Transformer.
  
  In Detection Transformer, an encoder is used to process output features of
  neck, then several queries interact with the encoder features using a
  decoder and do the regression and classification with the bounding box
  head.
  
  Args:
      backbone (:obj:`ConfigDict` or dict): Config of the backbone.
      neck (:obj:`ConfigDict` or dict, optional): Config of the neck.
          Defaults to None.
      encoder (:obj:`ConfigDict` or dict, optional): Config of the
          Transformer encoder. Defaults to None.
      decoder (:obj:`ConfigDict` or dict, optional): Config of the
          Transformer decoder. Defaults to None.
      bbox_head (:obj:`ConfigDict` or dict, optional): Config for the
          bounding box head module. Defaults to None.
      positional_encoding (:obj:`ConfigDict` or dict, optional): Config
          of the positional encoding module. Defaults to None.
      num_queries (int, optional): Number of decoder query in Transformer.
          Defaults to 100.
      train_cfg (:obj:`ConfigDict` or dict, optional): Training config of
          the bounding box head module. Defaults to None.
      test_cfg (:obj:`ConfigDict` or dict, optional): Testing config of
          the bounding box head module. Defaults to None.
      data_preprocessor (dict or ConfigDict, optional): The pre-process
          config of :class:`BaseDataPreprocessor`.  it usually includes,
          ``pad_size_divisor``, ``pad_value``, ``mean`` and ``std``.
          Defaults to None.
      init_cfg (:obj:`ConfigDict` or dict, optional): the config to control
          the initialization. Defaults to None.
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docstr: Forward with Transformer encoder.
  
  Args:
      feat (Tensor): Sequential features, has shape (bs, num_feat_points,
          dim).
      feat_mask (Tensor): ByteTensor, the padding mask of the features,
          has shape (bs, num_feat_points).
      feat_pos (Tensor): The positional embeddings of the features, has
          shape (bs, num_feat_points, dim).
  
  Returns:
      dict: The dictionary of encoder outputs, which includes the
      `memory` of the encoder output and other algorithm-specific
      arguments.
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code: def forward_encoder(
      self, feat: Tensor, feat_mask: Tensor, feat_pos: Tensor, **kwargs
  ) -> Dict:
      """Forward with Transformer encoder.
  
      Args:
          feat (Tensor): Sequential features, has shape (bs, num_feat_points,
              dim).
          feat_mask (Tensor): ByteTensor, the padding mask of the features,
              has shape (bs, num_feat_points).
          feat_pos (Tensor): The positional embeddings of the features, has
              shape (bs, num_feat_points, dim).
  
      Returns:
          dict: The dictionary of encoder outputs, which includes the
          `memory` of the encoder output and other algorithm-specific
          arguments.
      """
      pass
  
file_path: mmdetection/mmdet/models/detectors/base_detr.py
imports: from abc import ABCMeta, abstractmethod
  from typing import Dict, List, Tuple, Union
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures import OptSampleList, SampleList
  from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
  from .base import BaseDetector
label_desc: Use image node_id 9 for calling the DetectionTransformer obj's underlying member method code with example usage: obj.forward_encoder(feat, feat_mask, feat_pos) without return types