class_docstr: Implementation of the residual block for the kernel predictor network inside each location variable convolution
  block (LVCBlock).
  
  Parameters:
      config: (`UnivNetConfig`):
          Config for the `UnivNetModel` model.
code: def forward(self, hidden_states: torch.FloatTensor):
      # hidden_states should have shape (batch_size, channels, seq_length)
      residual = hidden_states
      hidden_states = self.dropout(hidden_states)
      hidden_states = self.conv1(hidden_states)
      hidden_states = nn.functional.leaky_relu(
          hidden_states, self.leaky_relu_slope
      )
      hidden_states = self.conv2(hidden_states)
      hidden_states = nn.functional.leaky_relu(
          hidden_states, self.leaky_relu_slope
      )
      return hidden_states + residual
  
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 UnivNetKernelPredictorResidualBlock obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: unknown