code: def __init__(self, output_channels=40):
      super(PointTransformerCLS, self).__init__()
      self.conv1 = nn.Conv1d(3, 64, kernel_size=1, bias=False)
      self.conv2 = nn.Conv1d(64, 64, kernel_size=1, bias=False)
      self.bn1 = nn.BatchNorm1d(64)
      self.bn2 = nn.BatchNorm1d(64)
      self.g_op0 = TransitionDown(
          in_channels=128, out_channels=128, n_neighbor=32
      )
      self.g_op1 = TransitionDown(
          in_channels=256, out_channels=256, n_neighbor=32
      )
  
      self.pt_last = PCTPositionEmbedding()
  
      self.relu = nn.ReLU()
      self.conv_fuse = nn.Sequential(
          nn.Conv1d(1280, 1024, kernel_size=1, bias=False),
          nn.BatchNorm1d(1024),
          nn.LeakyReLU(negative_slope=0.2),
      )
  
      self.linear1 = nn.Linear(1024, 512, bias=False)
      self.bn6 = nn.BatchNorm1d(512)
      self.dp1 = nn.Dropout(p=0.5)
      self.linear2 = nn.Linear(512, 256)
      self.bn7 = nn.BatchNorm1d(256)
      self.dp2 = nn.Dropout(p=0.5)
      self.linear3 = nn.Linear(256, output_channels)
  
imports: import torch
  from helper import TransitionDown
  from torch import nn
label_desc: Use image node_id 1 to create a new PointTransformerCLS object from inherited base classes: nn with example: obj = PointTransformerCLS(output_channels)