code: def __init__(
      self,
      num_class,
      vote_aggregation_config,
      num_heading_bin,
      mean_size_arr,
      num_proposal,
      sampling,
  ):
      super().__init__()
  
      self.num_class = num_class
      self.num_heading_bin = num_heading_bin
      self.num_size_cluster = len(mean_size_arr)
      self.mean_size_arr = nn.Parameter(
          torch.Tensor(mean_size_arr), requires_grad=False
      )
      self.num_proposal = num_proposal
      self.sampling = sampling
  
      # Object proposal/detection
      # Objectness scores (2), center residual (3),
      # heading class+residual (num_heading_bin*2), size class+residual(num_size_cluster*4)
      assert (
          vote_aggregation_config.module_name == "PointNetMSGDown"
      ), "Proposal Module support only PointNet2 for now"
      params = OmegaConf.to_container(vote_aggregation_config)
      self.vote_aggregation = PointNetMSGDown(**params)
      pn2_output_nc = vote_aggregation_config.down_conv_nn[-1][-1]
      output_feat = (
          2
          + 3
          + num_heading_bin * 2
          + self.num_size_cluster * 4
          + self.num_class
      )
      mid_feat = (pn2_output_nc + output_feat) // 2
      self.conv1 = torch.nn.Conv1d(pn2_output_nc, pn2_output_nc, 1)
      self.conv2 = torch.nn.Conv1d(pn2_output_nc, mid_feat, 1)
      self.conv3 = torch.nn.Conv1d(mid_feat, output_feat, 1)
      self.bn1 = torch.nn.BatchNorm1d(pn2_output_nc)
      self.bn2 = torch.nn.BatchNorm1d(mid_feat)
  
file_path: torch-points3d/torch_points3d/modules/VoteNet/proposal_module.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from omegaconf import OmegaConf
  from torch_points3d.modules.pointnet2 import PointNetMSGDown
  import torch_points_kernels
  from .votenet_results import VoteNetResults
label_desc: Use image node_id 1 to create a new ProposalModule object from inherited base classes: nn with example: obj = ProposalModule(num_class, vote_aggregation_config, num_heading_bin, mean_size_arr, num_proposal, sampling)