class_docstr: Implementation of the location variable convolution (LVC) residual block for the UnivNet residual network.
  
  Parameters:
      config: (`UnivNetConfig`):
          Config for the `UnivNetModel` model.
      kernel_size (`int`):
          The kernel size for the dilated 1D convolutional layer.
      dilation (`int`):
          The dilation for the dilated 1D convolutional layer.
code: def forward(self, hidden_states, kernel, bias, hop_size=256):
      residual = hidden_states
      hidden_states = nn.functional.leaky_relu(
          hidden_states, self.leaky_relu_slope
      )
      hidden_states = self.conv(hidden_states)
      hidden_states = nn.functional.leaky_relu(
          hidden_states, self.leaky_relu_slope
      )
      hidden_states = self.location_variable_convolution(
          hidden_states, kernel, bias, hop_size=hop_size
      )
      # Gated activation unit
      hidden_states = torch.sigmoid(
          hidden_states[:, : self.hidden_channels, :]
      ) * torch.tanh(hidden_states[:, self.hidden_channels :, :])
      # Skip connection
      hidden_states = residual + hidden_states
  
      return hidden_states
  
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 2 for calling the UnivNetLvcResidualBlock obj's underlying member method code with example usage: obj.forward(hidden_states, kernel, bias, hop_size) and returns: hidden_states