code: def __init__(self, time_step, unit, multi_layer, stack_cnt=0):
      super(StockBlockLayer, self).__init__()
      self.time_step = time_step
      self.unit = unit
      self.stack_cnt = stack_cnt
      self.multi = multi_layer
      self.weight = nn.Parameter(
          torch.Tensor(
              1,
              3 + 1,
              1,
              self.time_step * self.multi,
              self.multi * self.time_step,
          )
      )  # [K+1, 1, in_c, out_c]
      nn.init.xavier_normal_(self.weight)
      self.forecast = nn.Linear(
          self.time_step * self.multi, self.time_step * self.multi
      )
      self.forecast_result = nn.Linear(
          self.time_step * self.multi, self.time_step
      )
      if self.stack_cnt == 0:
          self.backcast = nn.Linear(
              self.time_step * self.multi, self.time_step
          )
      self.backcast_short_cut = nn.Linear(
          self.time_step, self.time_step
      )
      self.relu = nn.ReLU()
      self.GLUs = nn.ModuleList()
      self.output_channel = 4 * self.multi
      for i in range(3):
          if i == 0:
              self.GLUs.append(
                  GLU(
                      self.time_step * 4,
                      self.time_step * self.output_channel,
                  )
              )
              self.GLUs.append(
                  GLU(
                      self.time_step * 4,
                      self.time_step * self.output_channel,
                  )
              )
          elif i == 1:
              self.GLUs.append(
                  GLU(
                      self.time_step * self.output_channel,
                      self.time_step * self.output_channel,
                  )
              )
              self.GLUs.append(
                  GLU(
                      self.time_step * self.output_channel,
                      self.time_step * self.output_channel,
                  )
              )
          else:
              self.GLUs.append(
                  GLU(
                      self.time_step * self.output_channel,
                      self.time_step * self.output_channel,
                  )
              )
              self.GLUs.append(
                  GLU(
                      self.time_step * self.output_channel,
                      self.time_step * self.output_channel,
                  )
              )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from ..losses.pytorch import MAE
  from ..common._base_multivariate import BaseMultivariate
label_desc: Use image node_id 1 to create a new StockBlockLayer object from inherited base classes: nn with example: obj = StockBlockLayer(time_step, unit, multi_layer, stack_cnt)