code: def __init__(
      self,
      num_classes,
      in_channels,
      ocr_mid_channels=512,
      ocr_key_channels=256,
  ):
      super().__init__()
  
      self.indices = [-2, -1] if len(in_channels) > 1 else [-1, -1]
      self.conv3x3_ocr = layers.ConvBNReLU(
          in_channels[self.indices[1]],
          ocr_mid_channels,
          kernel_size=3,
          stride=1,
          padding=1,
      )
      self.ocr_gather_head = SpatialGatherModule(num_classes)
      self.ocr_distri_head = SpatialOCRModule(
          in_channels=ocr_mid_channels,
          key_channels=ocr_key_channels,
          out_channels=ocr_mid_channels,
          scale=1,
          dropout=0.05,
      )
      self.cls_head = nn.Conv2D(
          ocr_mid_channels,
          num_classes,
          kernel_size=1,
          stride=1,
          padding=0,
          bias_attr=True,
      )
      self.aux_head = nn.Sequential(
          layers.ConvBNReLU(
              in_channels[self.indices[0]],
              in_channels[self.indices[0]],
              kernel_size=1,
              stride=1,
              padding=0,
          ),
          nn.Conv2D(
              in_channels[self.indices[0]],
              num_classes,
              kernel_size=1,
              stride=1,
              padding=0,
              bias_attr=True,
          ),
      )
      self.init_weight()
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
label_desc: Use image node_id 1 to create a new OCRHead object from inherited base classes: nn with example: obj = OCRHead(num_classes, in_channels, ocr_mid_channels, ocr_key_channels)