code: def __init__(
      self,
      output_classes,
      input_dims=3,
      conv1_dim=64,
      dropout_prob=0.5,
      use_transform=True,
  ):
      super(PointNetCls, self).__init__()
      self.input_dims = input_dims
      self.conv1 = nn.ModuleList()
      self.conv1.append(nn.Conv1d(input_dims, conv1_dim, 1))
      self.conv1.append(nn.Conv1d(conv1_dim, conv1_dim, 1))
      self.conv1.append(nn.Conv1d(conv1_dim, conv1_dim, 1))
  
      self.bn1 = nn.ModuleList()
      self.bn1.append(nn.BatchNorm1d(conv1_dim))
      self.bn1.append(nn.BatchNorm1d(conv1_dim))
      self.bn1.append(nn.BatchNorm1d(conv1_dim))
  
      self.conv2 = nn.ModuleList()
      self.conv2.append(nn.Conv1d(conv1_dim, conv1_dim * 2, 1))
      self.conv2.append(nn.Conv1d(conv1_dim * 2, conv1_dim * 16, 1))
  
      self.bn2 = nn.ModuleList()
      self.bn2.append(nn.BatchNorm1d(conv1_dim * 2))
      self.bn2.append(nn.BatchNorm1d(conv1_dim * 16))
  
      self.maxpool = nn.MaxPool1d(conv1_dim * 16)
      self.pool_feat_len = conv1_dim * 16
  
      self.mlp3 = nn.ModuleList()
      self.mlp3.append(nn.Linear(conv1_dim * 16, conv1_dim * 8))
      self.mlp3.append(nn.Linear(conv1_dim * 8, conv1_dim * 4))
  
      self.bn3 = nn.ModuleList()
      self.bn3.append(nn.BatchNorm1d(conv1_dim * 8))
      self.bn3.append(nn.BatchNorm1d(conv1_dim * 4))
  
      self.dropout = nn.Dropout(0.3)
      self.mlp_out = nn.Linear(conv1_dim * 4, output_classes)
  
      self.use_transform = use_transform
      if use_transform:
          self.transform1 = TransformNet(input_dims)
          self.trans_bn1 = nn.BatchNorm1d(input_dims)
          self.transform2 = TransformNet(conv1_dim)
          self.trans_bn2 = nn.BatchNorm1d(conv1_dim)
  
file_path: dgl/examples/pytorch/pointcloud/pointnet/pointnet_cls.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
label_desc: Use image node_id 1 to create a new PointNetCls object from inherited base classes: nn with example: obj = PointNetCls(output_classes, input_dims, conv1_dim, dropout_prob, use_transform)