code: def __init__(
      self, nclass, oc_arch, norm_layer=nn.BatchNorm2d, **kwargs
  ):
      super(_OCHead, self).__init__()
      if oc_arch == "base":
          self.context = nn.Sequential(
              nn.Conv2d(2048, 512, 3, 1, padding=1, bias=False),
              norm_layer(512),
              nn.ReLU(True),
              BaseOCModule(
                  512,
                  512,
                  256,
                  256,
                  scales=([1]),
                  norm_layer=norm_layer,
                  **kwargs,
              ),
          )
      elif oc_arch == "pyramid":
          self.context = nn.Sequential(
              nn.Conv2d(2048, 512, 3, 1, padding=1, bias=False),
              norm_layer(512),
              nn.ReLU(True),
              PyramidOCModule(
                  512,
                  512,
                  256,
                  512,
                  scales=([1, 2, 3, 6]),
                  norm_layer=norm_layer,
                  **kwargs,
              ),
          )
      elif oc_arch == "asp":
          self.context = ASPOCModule(
              2048, 512, 256, 512, norm_layer=norm_layer, **kwargs
          )
      else:
          raise ValueError("Unknown OC architecture!")
  
      self.out = nn.Conv2d(512, nclass, 1)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
  from pywick.models.segmentation.da_basenets.fcn import _FCNHead
label_desc: Use image node_id 1 to create a new _OCHead object from inherited base classes: nn with example: obj = _OCHead(nclass, oc_arch, norm_layer)