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 __init__(
      self,
      config: UnivNetConfig,
      conv_kernel_size: int = 3,
      conv_layers: int = 4,
  ):
      super().__init__()
  
      self.conv_in_channels = config.model_hidden_channels
      self.conv_out_channels = 2 * config.model_hidden_channels
      self.conv_kernel_size = conv_kernel_size
      self.conv_layers = conv_layers
  
      self.kernel_channels = (
          self.conv_in_channels
          * self.conv_out_channels
          * self.conv_kernel_size
          * self.conv_layers
      )
      self.bias_channels = self.conv_out_channels * self.conv_layers
  
      self.resnet_in_channels = config.num_mel_bins
      self.resnet_hidden_channels = (
          config.kernel_predictor_hidden_channels
      )
      self.resnet_kernel_size = config.kernel_predictor_conv_size
      self.num_blocks = config.kernel_predictor_num_blocks
  
      self.leaky_relu_slope = config.leaky_relu_slope
  
      padding = (self.resnet_kernel_size - 1) // 2
  
      self.input_conv = nn.Conv1d(
          self.resnet_in_channels,
          self.resnet_hidden_channels,
          5,
          padding=2,
          bias=True,
      )
  
      self.resblocks = nn.ModuleList(
          [
              UnivNetKernelPredictorResidualBlock(config)
              for _ in range(self.num_blocks)
          ]
      )
  
      self.kernel_conv = nn.Conv1d(
          self.resnet_hidden_channels,
          self.kernel_channels,
          self.resnet_kernel_size,
          padding=padding,
          bias=True,
      )
      self.bias_conv = nn.Conv1d(
          self.resnet_hidden_channels,
          self.bias_channels,
          self.resnet_kernel_size,
          padding=padding,
          bias=True,
      )
  
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 UnivNetKernelPredictor object from inherited base classes: nn with example: obj = UnivNetKernelPredictor(config, conv_kernel_size, conv_layers)