code: def _make_conv_level(
      self, inplanes, planes, convs, stride=1, dilation=1
  ):
      modules = []
      for i in range(convs):
          modules.extend(
              [
                  nn.Conv2d(
                      inplanes,
                      planes,
                      kernel_size=3,
                      stride=stride if i == 0 else 1,
                      padding=dilation,
                      bias=False,
                      dilation=dilation,
                  ),
                  nn.BatchNorm2d(planes),
                  nn.ReLU(inplace=True),
              ]
          )
          inplanes = planes
      return nn.Sequential(*modules)
  
imports: import math
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the DLA obj's underlying member method code with example usage: obj._make_conv_level(inplanes, planes, convs, stride, dilation) and returns: nn