code: def _get_encoder_input(self, feats):
      # get projection features
      proj_feats = [
          self.input_proj[i](feat) for i, feat in enumerate(feats)
      ]
      if self.num_levels > len(proj_feats):
          len_srcs = len(proj_feats)
          for i in range(len_srcs, self.num_levels):
              if i == len_srcs:
                  proj_feats.append(self.input_proj[i](feats[-1]))
              else:
                  proj_feats.append(self.input_proj[i](proj_feats[-1]))
  
      # get encoder inputs
      feat_flatten = []
      spatial_shapes = []
      level_start_index = [
          0,
      ]
      for i, feat in enumerate(proj_feats):
          _, _, h, w = feat.shape
          # [b, c, h, w] -> [b, h*w, c]
          feat_flatten.append(feat.flatten(2).transpose([0, 2, 1]))
          # [num_levels, 2]
          spatial_shapes.append([h, w])
          # [l], start index of each level
          level_start_index.append(h * w + level_start_index[-1])
  
      # [b, l, c]
      feat_flatten = paddle.concat(feat_flatten, 1)
      level_start_index.pop()
      return (feat_flatten, spatial_shapes, level_start_index)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register
  from ..layers import MultiHeadAttention
  from ..heads.detr_head import MLP
  from .deformable_transformer import MSDeformableAttention
  from ..initializer import linear_init_, constant_, xavier_uniform_, normal_, bias_init_with_prob
  from .utils import _get_clones, get_sine_pos_embed, get_contrastive_denoising_training_group, inverse_sigmoid
label_desc: Use image node_id 5 for calling the RTDETRTransformer obj's underlying member method code with example usage: obj._get_encoder_input(feats) and returns: feat_flatten,  spatial_shapes,  level_start_index