class_docstr: Implementation of the location variable convolution (LVC) residual block for the UnivNet residual network.
  
  Parameters:
      config: (`UnivNetConfig`):
          Config for the `UnivNetModel` model.
      kernel_size (`int`):
          The kernel size for the dilated 1D convolutional layer.
      dilation (`int`):
          The dilation for the dilated 1D convolutional layer.
code: def __init__(
      self,
      config: UnivNetConfig,
      kernel_size: int,
      dilation: int,
  ):
      super().__init__()
      self.hidden_channels = config.model_hidden_channels
      self.kernel_size = kernel_size
      self.dilation = dilation
      self.leaky_relu_slope = config.leaky_relu_slope
  
      padding = self.dilation * (self.kernel_size - 1) // 2
  
      self.conv = nn.Conv1d(
          self.hidden_channels,
          self.hidden_channels,
          self.kernel_size,
          padding=padding,
          dilation=self.dilation,
      )
  
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 UnivNetLvcResidualBlock object from inherited base classes: nn with example: obj = UnivNetLvcResidualBlock(config, kernel_size, dilation)