code: def __init__(
      self, feature_strides, in_channels, embedding_dim, dropout_ratio
  ):
      super(SegformerSegmentationHead, self).__init__()
      assert len(feature_strides) == len(in_channels)
      assert min(feature_strides) == feature_strides[0]
      args = get_args()
      self.feature_strides = feature_strides
      self.in_channels = in_channels
      self.embedding_dim = embedding_dim
      self.num_classes = args.num_classes
      self.dropout_ratio = dropout_ratio
  
      (
          c1_in_channels,
          c2_in_channels,
          c3_in_channels,
          c4_in_channels,
      ) = self.in_channels
  
      self.linear_c4 = MLP(
          input_dim=c4_in_channels, embed_dim=self.embedding_dim
      )
      self.linear_c3 = MLP(
          input_dim=c3_in_channels, embed_dim=self.embedding_dim
      )
      self.linear_c2 = MLP(
          input_dim=c2_in_channels, embed_dim=self.embedding_dim
      )
      self.linear_c1 = MLP(
          input_dim=c1_in_channels, embed_dim=self.embedding_dim
      )
  
      self.conv_fuse = torch.nn.Conv2d(
          self.embedding_dim * 4, self.embedding_dim, 1, 1
      )
      self.norm = apex.parallel.SyncBatchNorm(self.embedding_dim)
  
      self.dropout = torch.nn.Dropout2d(self.dropout_ratio)
      self.linear_pred = torch.nn.Conv2d(
          self.embedding_dim, self.num_classes, kernel_size=1
      )
  
imports: import math
  import einops
  import torch
  import apex
  import torch.nn.functional
  from megatron import get_args
  from megatron.model import LayerNorm
  from megatron.model.module import MegatronModule
  from megatron.model.vision.utils import resize
label_desc: Use image node_id 1 to create a new SegformerSegmentationHead object from inherited base classes: MegatronModule with example: obj = SegformerSegmentationHead(feature_strides, in_channels, embedding_dim, dropout_ratio)