class_docstr: Implementation of the location variable convolution (LVC) residual block of the UnivNet residual block. Includes a
  `UnivNetKernelPredictor` inside to predict the kernels and biases of the LVC layers.
  
  Based on LVCBlock in
  [maum-ai/univnet](https://github.com/maum-ai/univnet/blob/9bb2b54838bb6d7ce767131cc7b8b61198bc7558/model/lvcnet.py#L98)
  
  Parameters:
      config (`UnivNetConfig`):
          Config for the `UnivNetModel` model.
      layer_id (`int`):
          An integer corresponding to the index of the current LVC resnet block layer. This should be between 0 and
          `len(config.resblock_stride_sizes) - 1)` inclusive.
      lvc_hop_size (`int`, *optional*, defaults to 256):
          The hop size for the location variable convolutional layers.
code: def __init__(
      self,
      config: UnivNetConfig,
      layer_id: int,
      lvc_hop_size: int = 256,
  ):
      super().__init__()
      self.hidden_channels = config.model_hidden_channels
      self.kernel_size = config.resblock_kernel_sizes[layer_id]
      self.stride = config.resblock_stride_sizes[layer_id]
      self.dilations = config.resblock_dilation_sizes[layer_id]
      self.cond_hop_length = lvc_hop_size
      self.leaky_relu_slope = config.leaky_relu_slope
      self.num_blocks = len(self.dilations)
  
      self.convt_pre = nn.ConvTranspose1d(
          self.hidden_channels,
          self.hidden_channels,
          2 * self.stride,
          stride=self.stride,
          padding=self.stride // 2 + self.stride % 2,
          output_padding=self.stride % 2,
      )
  
      self.kernel_predictor = UnivNetKernelPredictor(
          config, self.kernel_size, self.num_blocks
      )
  
      self.resblocks = nn.ModuleList(
          [
              UnivNetLvcResidualBlock(
                  config, self.kernel_size, self.dilations[i]
              )
              for i in range(self.num_blocks)
          ]
      )
  
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 UnivNetLvcBlock object from inherited base classes: nn with example: obj = UnivNetLvcBlock(config, layer_id, lvc_hop_size)