code: def __init__(
      self,
      n_inputs,
      n_outputs,
      kernel_size,
      stride,
      dilation,
      padding,
      dropout=0.1,
      init=True,
  ):
      super(TemporalBlock, self).__init__()
      self.kernel_size = kernel_size
      self.conv1 = weight_norm(
          nn.Conv1d(
              n_inputs,
              n_outputs,
              kernel_size,
              stride=stride,
              padding=padding,
              dilation=dilation,
          )
      )
      self.chomp1 = Chomp1d(padding)
      self.relu1 = nn.ReLU()
      self.dropout1 = nn.Dropout(dropout)
  
      self.conv2 = weight_norm(
          nn.Conv1d(
              n_outputs,
              n_outputs,
              kernel_size,
              stride=stride,
              padding=padding,
              dilation=dilation,
          )
      )
      self.chomp2 = Chomp1d(padding)
      self.relu2 = nn.ReLU()
      self.dropout2 = nn.Dropout(dropout)
  
      self.net = nn.Sequential(
          self.conv1,
          self.chomp1,
          self.relu1,
          self.dropout1,
          self.conv2,
          self.chomp2,
          self.relu2,
          self.dropout2,
      )
      self.downsample = (
          nn.Conv1d(n_inputs, n_outputs, 1)
          if n_inputs != n_outputs
          else None
      )
      self.init = init
      self.relu = nn.ReLU()
      self.init_weights()
  
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 zoo.chronos.model.tcmf.data_loader import TCMFDataLoader
  from zoo.chronos.model.tcmf.time import TimeCovariates
  import logging
label_desc: Use image node_id 1 to create a new TemporalBlock object from inherited base classes: nn with example: obj = TemporalBlock(n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout, init)