code: def __init__(
      self,
      out_classes,
      batch_size,
      n_points=2048,
      feature_dim=3,
      n_blocks=4,
      downsampling_rate=4,
      hidden_dim=32,
      transformer_dim=None,
      n_neighbors=16,
  ):
      super().__init__()
      self.backbone = PointTransformer(
          n_points,
          batch_size,
          feature_dim,
          n_blocks,
          downsampling_rate,
          hidden_dim,
          transformer_dim,
          n_neighbors,
      )
  
      self.fc = nn.Sequential(
          nn.Linear(32 * 2**n_blocks, 512),
          nn.ReLU(),
          nn.Linear(512, 512),
          nn.ReLU(),
          nn.Linear(512, 32 * 2**n_blocks),
      )
      self.ptb = PointTransformerBlock(
          32 * 2**n_blocks, n_neighbors, transformer_dim
      )
  
      self.n_blocks = n_blocks
      self.transition_ups = nn.ModuleList()
      self.transformers = nn.ModuleList()
      for i in reversed(range(n_blocks)):
          block_hidden_dim = 32 * 2**i
          self.transition_ups.append(
              TransitionUp(
                  block_hidden_dim * 2,
                  block_hidden_dim,
                  block_hidden_dim,
              )
          )
          self.transformers.append(
              PointTransformerBlock(
                  block_hidden_dim, n_neighbors, transformer_dim
              )
          )
  
      self.out = nn.Sequential(
          nn.Linear(32 + 16, 64),
          nn.ReLU(),
          nn.Linear(64, 64),
          nn.ReLU(),
          nn.Linear(64, out_classes),
      )
  
imports: import numpy
  import torch
  from helper import index_points, square_distance, TransitionDown, TransitionUp
  from torch import nn
label_desc: Use image node_id 1 to create a new PointTransformerSeg object from inherited base classes: nn with example: obj = PointTransformerSeg(out_classes, batch_size, n_points, feature_dim, n_blocks, downsampling_rate, hidden_dim, transformer_dim, n_neighbors)