code: def __init__(
      self,
      channels,
      bias=False,
      bn=True,
      activation=nn.LeakyReLU(negative_slope=0.01),
  ):
      super().__init__()
      for i in range(len(channels) - 1):
          self.append(
              Conv2D(
                  channels[i],
                  channels[i + 1],
                  bn=bn,
                  bias=bias,
                  activation=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 MLP2D object from inherited base classes: Seq with example: obj = MLP2D(channels, bias, bn, activation)