class_docstr: Implementation of the kernel predictor network which supplies the kernel and bias for the location variable
  convolutional layers (LVCs) in each UnivNet LVCBlock.
  
  Based on the KernelPredictor implementation in
  [maum-ai/univnet](https://github.com/maum-ai/univnet/blob/9bb2b54838bb6d7ce767131cc7b8b61198bc7558/model/lvcnet.py#L7).
  
  Parameters:
      config: (`UnivNetConfig`):
          Config for the `UnivNetModel` model.
      conv_kernel_size (`int`, *optional*, defaults to 3):
          The kernel size for the location variable convolutional layer kernels (convolutional weight tensor).
      conv_layers (`int`, *optional*, defaults to 4):
          The number of location variable convolutional layers to output kernels and biases for.
code: def remove_weight_norm(self):
      nn.utils.remove_weight_norm(self.input_conv)
      for layer in self.resblocks:
          layer.remove_weight_norm()
      nn.utils.remove_weight_norm(self.kernel_conv)
      nn.utils.remove_weight_norm(self.bias_conv)
  
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 UnivNetKernelPredictor obj's underlying member method code with example usage: obj.remove_weight_norm() without return types