class_docstr: A base class for Neural Radiance Fields (NeRF) models.
  
  Args:
      num_input_dims (int): Number of input dimensions.
      bound (torch.Tensor): The bounding box tensor.
      density_activation (DensityActivationEnum): Activation function for density.
      blob_radius (float): Radius for the blob.
      blob_density (float): Density for the blob.
      normal_type (Optional[NormalTypeEnum]): Method to compute normals.
class_docstr_tok: ["A","base","class","for","Neural","Radiance","Fields","(","NeRF",")","models",".","Args",":","num_input_dims","(","int",")",":","Number","of","input","dimensions",".","bound","(","torch.Tensor",")",":","The","bounding","box","tensor",".","density_activation","(","DensityActivationEnum",")",":","Activation","function","for","density",".","blob_radius","(","float",")",":","Radius","for","the","blob",".","blob_density","(","float",")",":","Density","for","the","blob",".","normal_type","(","Optional","[","NormalTypeEnum","]",")",":","Method","to","compute","normals","."]
docstr: Calculate sigma (density). To be implemented by subclasses.
docstr_tok: ["Calculate","sigma","(","density",")",".","To","be","implemented","by","subclasses","."]
code: def sigma_net(self, positions_encoding: torch.Tensor) -> torch.Tensor:
      """Calculate sigma (density). To be implemented by subclasses."""
      raise NotImplementedError
  
imports: from enum import Enum
  from typing import Optional, Tuple
  import mcubes
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import trimesh
  from nemo.collections.multimodal.modules.nerf.utils.activation import trunc_exp
label_desc: Use image node_id 3 for calling the NeRFBase obj's underlying member method code with example usage: obj.sigma_net(positions_encoding) without return types