code: def __init__(
      self,
      in_channels,
      out_channels,
      bias=True,
      bn=True,
      activation=nn.LeakyReLU(negative_slope=0.01),
  ):
      super().__init__()
      self.append(
          nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=bias)
      )
      if bn:
          self.append(nn.BatchNorm1d(out_channels))
      if activation:
          self.append(activation)
  
file_path: torch-points3d/torch_points3d/core/common_modules/dense_modules.py
imports: import torch.nn
  from .base_modules import Seq
label_desc: Use image node_id 1 to create a new Conv1D object from inherited base classes: Seq with example: obj = Conv1D(in_channels, out_channels, bias, bn, activation)