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.
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docstr: Forward pass for the NeRF model.
  
  Args:
      positions (torch.Tensor): The positions.
      return_normal (bool): Flag to indicate whether to return normals or not.
  
  Returns:
      Tuple containing density, features, and possibly normals.
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code: def forward(
      self, positions: torch.Tensor, return_normal: bool = True
  ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
      """
      Forward pass for the NeRF model.
  
      Args:
          positions (torch.Tensor): The positions.
          return_normal (bool): Flag to indicate whether to return normals or not.
  
      Returns:
          Tuple containing density, features, and possibly normals.
      """
  
      if return_normal:
          if self.normal_type == NormalTypeEnum.AUTOGRAD:
              with torch.enable_grad():
                  positions.requires_grad_(True)
                  sigma, features = self.forward_density_features(
                      positions
                  )
                  normal = -torch.autograd.grad(
                      torch.sum(sigma), positions, create_graph=True
                  )[
                      0
                  ]  # [N, D]
          elif self.normal_type in [
              NormalTypeEnum.CENTRAL_FINITE_DIFFERENCE,
              NormalTypeEnum.FORWARD_FINITE_DIFFERENCE,
              NormalTypeEnum.BACKWARD_FINITE_DIFFERENCE,
          ]:
              sigma, features = self.forward_density_features(positions)
              normal = self.normal_finite_differences(positions)
          else:
              raise NotImplementedError("Invalid normal type.")
  
          normal = F.normalize(normal)
          normal = torch.nan_to_num(normal)
      else:
          sigma, features = self.forward_density_features(positions)
          normal = None
  
      return sigma, features, normal
  
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 5 for calling the NeRFBase obj's underlying member method code with example usage: obj.forward(positions, return_normal) and returns: sigma,  features,  normal