code: def __init__(
      self,
      n_points,
      batch_size,
      feature_dim=3,
      n_blocks=4,
      downsampling_rate=4,
      hidden_dim=32,
      transformer_dim=None,
      n_neighbors=16,
  ):
      super(PointTransformer, self).__init__()
      self.fc = nn.Sequential(
          nn.Linear(feature_dim, hidden_dim),
          nn.ReLU(),
          nn.Linear(hidden_dim, hidden_dim),
      )
      self.ptb = PointTransformerBlock(
          hidden_dim, n_neighbors, transformer_dim
      )
      self.transition_downs = nn.ModuleList()
      self.transformers = nn.ModuleList()
      for i in range(n_blocks):
          block_hidden_dim = hidden_dim * 2 ** (i + 1)
          block_n_points = n_points // (downsampling_rate ** (i + 1))
          self.transition_downs.append(
              TransitionDown(
                  block_n_points,
                  batch_size,
                  [
                      block_hidden_dim // 2 + 3,
                      block_hidden_dim,
                      block_hidden_dim,
                  ],
                  n_neighbors=n_neighbors,
              )
          )
          self.transformers.append(
              PointTransformerBlock(
                  block_hidden_dim, n_neighbors, transformer_dim
              )
          )
  
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 PointTransformer object from inherited base classes: nn with example: obj = PointTransformer(n_points, batch_size, feature_dim, n_blocks, downsampling_rate, hidden_dim, transformer_dim, n_neighbors)