code: def __init__(
      self,
      num_inputs,
      num_channels,
      kernel_size=2,
      dropout=0.1,
      init=True,
  ):
      super(TemporalConvNet, self).__init__()
      layers = []
      self.num_channels = num_channels
      self.num_inputs = num_inputs
      self.kernel_size = kernel_size
      self.dropout = dropout
      num_levels = len(num_channels)
      for i in range(num_levels):
          dilation_size = 2**i
          in_channels = num_inputs if i == 0 else num_channels[i - 1]
          out_channels = num_channels[i]
          if i == num_levels - 1:
              layers += [
                  TemporalBlockLast(
                      in_channels,
                      out_channels,
                      kernel_size,
                      stride=1,
                      dilation=dilation_size,
                      padding=(kernel_size - 1) * dilation_size,
                      dropout=dropout,
                      init=init,
                  )
              ]
          else:
              layers += [
                  TemporalBlock(
                      in_channels,
                      out_channels,
                      kernel_size,
                      stride=1,
                      dilation=dilation_size,
                      padding=(kernel_size - 1) * dilation_size,
                      dropout=dropout,
                      init=init,
                  )
              ]
  
      self.network = nn.Sequential(*layers)
  
imports: import pickle
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from torch.autograd import Variable
  from torch.nn.utils import weight_norm
  from bigdl.chronos.model.tcmf.data_loader import TCMFDataLoader
  from bigdl.chronos.model.tcmf.time import TimeCovariates
  import logging
label_desc: Use image node_id 1 to create a new TemporalConvNet object from inherited base classes: nn with example: obj = TemporalConvNet(num_inputs, num_channels, kernel_size, dropout, init)