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","."]
code: def __init__(
      self, num_input_dims: int, num_output_dims: int, bias: bool = True
  ):
      super().__init__()
  
      self.dense = nn.Linear(num_input_dims, num_output_dims, bias=bias)
      self.norm = nn.LayerNorm(num_output_dims)
      self.activation = nn.SiLU(inplace=True)
  
      if num_input_dims != num_output_dims:
          self.skip = nn.Linear(
              num_input_dims, num_output_dims, bias=False
          )
      else:
          self.skip = None
  
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 1 to create a new ResBlock object from inherited base classes: nn with example: obj = ResBlock(num_input_dims, num_output_dims, bias)