class_docstr: DLA, see https://arxiv.org/pdf/1707.06484.pdf
  
  Args:
      depth (int): DLA depth, only support 34 now.
      residual_root (bool): whether use a reidual layer in the root block
      pre_img (bool): add pre_img, only used in CenterTrack
      pre_hm (bool): add pre_hm, only used in CenterTrack
class_docstr_tok: ["DLA",",","see","https",":","\/\/arxiv.org\/pdf\/1707.06484.pdf","Args",":","depth","(","int",")",":","DLA","depth",",","only","support","34","now",".","residual_root","(","bool",")",":","whether","use","a","reidual","layer","in","the","root","block","pre_img","(","bool",")",":","add","pre_img",",","only","used","in","CenterTrack","pre_hm","(","bool",")",":","add","pre_hm",",","only","used","in","CenterTrack"]
code: def _make_conv_level(self, ch_in, ch_out, conv_num, stride=1):
      modules = []
      for i in range(conv_num):
          modules.extend(
              [
                  ConvNormLayer(
                      ch_in,
                      ch_out,
                      filter_size=3,
                      stride=stride if i == 0 else 1,
                      bias_on=False,
                      norm_decay=None,
                  ),
                  nn.ReLU(),
              ]
          )
          ch_in = ch_out
      return nn.Sequential(*modules)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling.layers import ConvNormLayer
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 2 for calling the DLA obj's underlying member method code with example usage: obj._make_conv_level(ch_in, ch_out, conv_num, stride) and returns: nn