code: def __init__(
      self,
      target_dim,
      cond_length,
      time_emb_dim=16,
      residual_layers=8,
      residual_channels=8,
      dilation_cycle_length=2,
      residual_hidden=64,
  ):
      super().__init__()
      self.input_projection = nn.Conv1d(
          1, residual_channels, 1, padding=2, padding_mode="circular"
      )
      self.diffusion_embedding = DiffusionEmbedding(
          time_emb_dim, proj_dim=residual_hidden
      )
      self.cond_upsampler = CondUpsampler(
          target_dim=target_dim, cond_length=cond_length
      )
      self.residual_layers = nn.ModuleList(
          [
              ResidualBlock(
                  residual_channels=residual_channels,
                  dilation=2 ** (i % dilation_cycle_length),
                  hidden_size=residual_hidden,
              )
              for i in range(residual_layers)
          ]
      )
      self.skip_projection = nn.Conv1d(
          residual_channels, residual_channels, 3
      )
      self.output_projection = nn.Conv1d(residual_channels, 1, 3)
  
      nn.init.kaiming_normal_(self.input_projection.weight)
      nn.init.kaiming_normal_(self.skip_projection.weight)
      nn.init.zeros_(self.output_projection.weight)
  
imports: import math
  import torch
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new EpsilonTheta object from inherited base classes: nn with example: obj = EpsilonTheta(target_dim, cond_length, time_emb_dim, residual_layers, residual_channels, dilation_cycle_length, residual_hidden)