class_docstr: Kernel predictor for the location-variable convolutions
class_docstr_tok: ["Kernel","predictor","for","the","location-variable","convolutions"]
docstr: Args:
      c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
  Returns:
docstr_tok: ["Args",":","c","(","Tensor",")",":","the","conditioning","sequence","(","batch",",","cond_channels",",","cond_length",")","Returns",":"]
code: def forward(self, c):
      """
      Args:
          c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
      Returns:
      """
      batch, _, cond_length = c.shape
  
      c = self.input_conv(c)
      c = c + self.residual_conv(c)
      k = self.kernel_conv(c)
      b = self.bias_conv(c)
  
      kernels = k.contiguous().view(
          batch,
          self.conv_layers,
          self.conv_in_channels,
          self.conv_out_channels,
          self.conv_kernel_size,
          cond_length,
      )
      bias = b.contiguous().view(
          batch, self.conv_layers, self.conv_out_channels, cond_length
      )
      return kernels, bias
  
file_path: TTS/TTS/vocoder/layers/lvc_block.py
imports: import torch
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the KernelPredictor obj's underlying member method code with example usage: obj.forward(c) and returns: kernels,  bias