docstr: Applies a Transformer model on the inputs.
  
  Parameters:
      src (List(Tensor)): Backbone feature maps with shape [[bs, c, h, w]].
      src_mask (Tensor, optional): A tensor used in multi-head attention
          to prevents attention to some unwanted positions, usually the
          paddings or the subsequent positions. It is a tensor with shape
          [bs, H, W]`. When the data type is bool, the unwanted positions
          have `False` values and the others have `True` values. When the
          data type is int, the unwanted positions have 0 values and the
          others have 1 values. When the data type is float, the unwanted
          positions have `-INF` values and the others have 0 values. It
          can be None when nothing wanted or needed to be prevented
          attention to. Default None.
  
  Returns:
      output (Tensor): [num_levels, batch_size, num_queries, hidden_dim]
      memory (Tensor): [batch_size, hidden_dim, h, w]
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code: def forward(self, src, src_mask=None, *args, **kwargs):
      r"""
      Applies a Transformer model on the inputs.
  
      Parameters:
          src (List(Tensor)): Backbone feature maps with shape [[bs, c, h, w]].
          src_mask (Tensor, optional): A tensor used in multi-head attention
              to prevents attention to some unwanted positions, usually the
              paddings or the subsequent positions. It is a tensor with shape
              [bs, H, W]`. When the data type is bool, the unwanted positions
              have `False` values and the others have `True` values. When the
              data type is int, the unwanted positions have 0 values and the
              others have 1 values. When the data type is float, the unwanted
              positions have `-INF` values and the others have 0 values. It
              can be None when nothing wanted or needed to be prevented
              attention to. Default None.
  
      Returns:
          output (Tensor): [num_levels, batch_size, num_queries, hidden_dim]
          memory (Tensor): [batch_size, hidden_dim, h, w]
      """
      # use last level feature map
      src_proj = self.input_proj(src[-1])
      bs, c, h, w = paddle.shape(src_proj)
      # flatten [B, C, H, W] to [B, HxW, C]
      src_flatten = src_proj.flatten(2).transpose([0, 2, 1])
      if src_mask is not None:
          src_mask = F.interpolate(src_mask.unsqueeze(0), size=(h, w))[
              0
          ]
      else:
          src_mask = paddle.ones([bs, h, w])
      pos_embed = self.position_embedding(src_mask).flatten(1, 2)
  
      if self.training:
          src_mask = self._convert_attention_mask(src_mask)
          src_mask = src_mask.reshape([bs, 1, 1, h * w])
      else:
          src_mask = None
  
      memory = self.encoder(
          src_flatten, src_mask=src_mask, pos_embed=pos_embed
      )
  
      query_pos_embed = self.query_pos_embed.weight.unsqueeze(0).tile(
          [bs, 1, 1]
      )
      tgt = paddle.zeros_like(query_pos_embed)
      output = self.decoder(
          tgt,
          memory,
          memory_mask=src_mask,
          pos_embed=pos_embed,
          query_pos_embed=query_pos_embed,
      )
  
      if self.training:
          src_mask = src_mask.reshape([bs, 1, 1, h, w])
      else:
          src_mask = None
  
      return (
          output,
          memory.transpose([0, 2, 1]).reshape([bs, c, h, w]),
          src_proj,
          src_mask,
      )
  
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 ppdet.core.workspace import register
  from ..layers import MultiHeadAttention, _convert_attention_mask
  from .position_encoding import PositionEmbedding
  from .utils import _get_clones
  from ..initializer import linear_init_, conv_init_, xavier_uniform_, normal_
label_desc: Use image node_id 5 for calling the DETRTransformer obj's underlying member method code with example usage: obj.forward(src, src_mask) and returns: output,  memory,  src_proj,  src_mask