code: def __init__(self, init_cfg=None):
      super().__init__(init_cfg)
  
      self.convs = Sequential(
          ConvModule(
              in_channels=256,
              out_channels=256,
              kernel_size=3,
              padding=1,
              bias="auto",
              norm_cfg=dict(type="BN"),
              act_cfg=dict(type="ReLU"),
          ),
          ConvModule(
              in_channels=256,
              out_channels=256,
              kernel_size=3,
              padding=1,
              bias="auto",
              norm_cfg=dict(type="BN"),
              act_cfg=dict(type="ReLU"),
          ),
          ConvModule(
              in_channels=256,
              out_channels=256,
              kernel_size=3,
              padding=1,
              stride=(2, 1),
              bias="auto",
              norm_cfg=dict(type="GN", num_groups=32),
              act_cfg=dict(type="ReLU"),
          ),
          ConvModule(
              in_channels=256,
              out_channels=256,
              kernel_size=3,
              padding=1,
              stride=(2, 1),
              bias="auto",
              norm_cfg=dict(type="GN", num_groups=32),
              act_cfg=dict(type="ReLU"),
          ),
          nn.AdaptiveAvgPool2d((1, None)),
      )
  
imports: import torch.nn
  from mmcv.cnn import ConvModule
  from mmengine.model import BaseModule, Sequential
  from mmocr.registry import MODELS
label_desc: Use image node_id 1 to create a new ABCNetRecBackbone object from inherited base classes: BaseModule with example: obj = ABCNetRecBackbone(init_cfg)