class_docstr: Kernel predictor for the location-variable convolutions
  
  Args:
          cond_channels (int): number of channel for the conditioning sequence,
          conv_in_channels (int): number of channel for the input sequence,
          conv_out_channels (int): number of channel for the output sequence,
          conv_layers (int): number of layers
class_docstr_tok: ["Kernel","predictor","for","the","location-variable","convolutions","Args",":","cond_channels","(","int",")",":","number","of","channel","for","the","conditioning","sequence",",","conv_in_channels","(","int",")",":","number","of","channel","for","the","input","sequence",",","conv_out_channels","(","int",")",":","number","of","channel","for","the","output","sequence",",","conv_layers","(","int",")",":","number","of","layers"]
code: def remove_weight_norm(self):
      parametrize.remove_parametrizations(self.input_conv[0], "weight")
      parametrize.remove_parametrizations(self.kernel_conv, "weight")
      parametrize.remove_parametrizations(self.bias_conv, "weight")
      for block in self.residual_convs:
          parametrize.remove_parametrizations(block[1], "weight")
          parametrize.remove_parametrizations(block[3], "weight")
  
imports: import torch.nn
  from torch.nn.utils import parametrize
label_desc: Use image node_id 3 for calling the KernelPredictor obj's underlying member method code with example usage: obj.remove_weight_norm() without return types