class_docstr: A residual block module.
  
  Args:
      num_input_dims (int): Number of input dimensions.
      num_output_dims (int): Number of output dimensions.
      bias (bool): If True, enables the bias in Linear layers. Default is True.
class_docstr_tok: ["A","residual","block","module",".","Args",":","num_input_dims","(","int",")",":","Number","of","input","dimensions",".","num_output_dims","(","int",")",":","Number","of","output","dimensions",".","bias","(","bool",")",":","If","True",",","enables","the","bias","in","Linear","layers",".","Default","is","True","."]
docstr: Forward pass through the residual block.
  
  Args:
      x (torch.Tensor): Input tensor.
  
  Returns:
      torch.Tensor: Output tensor.
docstr_tok: ["Forward","pass","through","the","residual","block",".","Args",":","x","(","torch.Tensor",")",":","Input","tensor",".","Returns",":","torch.Tensor",":","Output","tensor","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Forward pass through the residual block.
  
      Args:
          x (torch.Tensor): Input tensor.
  
      Returns:
          torch.Tensor: Output tensor.
      """
      identity = x
  
      out = self.dense(x)
      out = self.norm(out)
  
      if self.skip is not None:
          identity = self.skip(identity)
  
      out += identity
      out = self.activation(out)
  
      return out
  
file_path: NeMo/nemo/collections/multimodal/modules/nerf/geometry/layers.py
imports: from typing import Callable, List, Type, Union
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the ResBlock obj's underlying member method code with example usage: obj.forward(x) and returns: out