class_docstr: the location-variable convolutions
class_docstr_tok: ["the","location-variable","convolutions"]
code: def __init__(  # pylint: disable=dangerous-default-value
      self,
      in_channels,
      cond_channels,
      stride,
      dilations=[1, 3, 9, 27],
      lReLU_slope=0.2,
      conv_kernel_size=3,
      cond_hop_length=256,
      kpnet_hidden_channels=64,
      kpnet_conv_size=3,
      kpnet_dropout=0.0,
  ):
      super().__init__()
  
      self.cond_hop_length = cond_hop_length
      self.conv_layers = len(dilations)
      self.conv_kernel_size = conv_kernel_size
  
      self.kernel_predictor = KernelPredictor(
          cond_channels=cond_channels,
          conv_in_channels=in_channels,
          conv_out_channels=2 * in_channels,
          conv_layers=len(dilations),
          conv_kernel_size=conv_kernel_size,
          kpnet_hidden_channels=kpnet_hidden_channels,
          kpnet_conv_size=kpnet_conv_size,
          kpnet_dropout=kpnet_dropout,
          kpnet_nonlinear_activation_params={
              "negative_slope": lReLU_slope
          },
      )
  
      self.convt_pre = nn.Sequential(
          nn.LeakyReLU(lReLU_slope),
          nn.utils.parametrizations.weight_norm(
              nn.ConvTranspose1d(
                  in_channels,
                  in_channels,
                  2 * stride,
                  stride=stride,
                  padding=stride // 2 + stride % 2,
                  output_padding=stride % 2,
              )
          ),
      )
  
      self.conv_blocks = nn.ModuleList()
      for dilation in dilations:
          self.conv_blocks.append(
              nn.Sequential(
                  nn.LeakyReLU(lReLU_slope),
                  nn.utils.parametrizations.weight_norm(
                      nn.Conv1d(
                          in_channels,
                          in_channels,
                          conv_kernel_size,
                          padding=dilation
                          * (conv_kernel_size - 1)
                          // 2,
                          dilation=dilation,
                      )
                  ),
                  nn.LeakyReLU(lReLU_slope),
              )
          )
  
imports: from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import parametrize
  from TTS.tts.layers.delightful_tts.kernel_predictor import KernelPredictor
label_desc: Use image node_id 1 to create a new LVCBlock object from inherited base classes: torch.nn with example: obj = LVCBlock(in_channels, cond_channels, stride, dilations, lReLU_slope, conv_kernel_size, cond_hop_length, kpnet_hidden_channels, kpnet_conv_size, kpnet_dropout)