code: def __init__(
      self,
      out_classes,
      batch_size,
      n_points=1024,
      feature_dim=3,
      n_blocks=4,
      downsampling_rate=4,
      hidden_dim=32,
      transformer_dim=None,
      n_neighbors=16,
  ):
      super(PointTransformerCLS, self).__init__()
      self.backbone = PointTransformer(
          n_points,
          batch_size,
          feature_dim,
          n_blocks,
          downsampling_rate,
          hidden_dim,
          transformer_dim,
          n_neighbors,
      )
      self.out = self.fc2 = nn.Sequential(
          nn.Linear(hidden_dim * 2 ** (n_blocks), 256),
          nn.ReLU(),
          nn.Linear(256, 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 PointTransformerCLS object from inherited base classes: nn with example: obj = PointTransformerCLS(out_classes, batch_size, n_points, feature_dim, n_blocks, downsampling_rate, hidden_dim, transformer_dim, n_neighbors)