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 remove_weight_norm(self):
      nn.utils.remove_weight_norm(self.convt_pre)
      self.kernel_predictor.remove_weight_norm()
      for layer in self.resblocks:
          layer.remove_weight_norm()
  
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 4 for calling the UnivNetLvcBlock obj's underlying member method code with example usage: obj.remove_weight_norm() without return types