code: def __init__(self, config: UnivNetConfig):
      super().__init__(config)
  
      self.num_kernels = len(config.resblock_kernel_sizes)
      self.leaky_relu_slope = config.leaky_relu_slope
  
      self.conv_pre = nn.Conv1d(
          config.model_in_channels,
          config.model_hidden_channels,
          kernel_size=7,
          stride=1,
          padding=3,
          padding_mode="reflect",
      )
  
      # Initialize location-variable convolution ResNet Blocks.
      num_layers = len(config.resblock_stride_sizes)
      hop_length = 1
      hop_lengths = []
      for stride in config.resblock_stride_sizes:
          hop_length = hop_length * stride
          hop_lengths.append(hop_length)
  
      self.resblocks = nn.ModuleList(
          [
              UnivNetLvcBlock(
                  config,
                  layer_id=i,
                  lvc_hop_size=hop_lengths[i],
              )
              for i in range(num_layers)
          ]
      )
  
      self.conv_post = nn.Conv1d(
          config.model_hidden_channels,
          1,
          7,
          padding=3,
          padding_mode="reflect",
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ...modeling_utils import ModelOutput, PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_univnet import UnivNetConfig
label_desc: Use image node_id 1 to create a new UnivNetModel object from inherited base classes: PreTrainedModel with example: obj = UnivNetModel(config)