code: def __init__(
      self,
      filter_channels,
      merge_layer=0,
      res_layers=[],
      norm="group",
      last_op=None,
  ):
      super(MLP, self).__init__()
  
      self.filters = nn.ModuleList()
      self.norms = nn.ModuleList()
      self.merge_layer = (
          merge_layer if merge_layer > 0 else len(filter_channels) // 2
      )
      self.res_layers = res_layers
      self.norm = norm
      self.last_op = last_op
  
      for l in range(0, len(filter_channels) - 1):
          if l in self.res_layers:
              self.filters.append(
                  nn.Conv1d(
                      filter_channels[l] + filter_channels[0],
                      filter_channels[l + 1],
                      1,
                  )
              )
          else:
              self.filters.append(
                  nn.Conv1d(
                      filter_channels[l], filter_channels[l + 1], 1
                  )
              )
          if l != len(filter_channels) - 2:
              if norm == "group":
                  self.norms.append(
                      nn.GroupNorm(32, filter_channels[l + 1])
                  )
              elif norm == "batch":
                  self.norms.append(
                      nn.BatchNorm1d(filter_channels[l + 1])
                  )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: nn with example: obj = MLP(filter_channels, merge_layer, res_layers, norm, last_op)