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: Calculate both density and features based on the input positions.
  
  This function takes into account edge cases like empty input tensors and calculates
  the density and features accordingly. See GitHub issues for details:
  - https://github.com/KAIR-BAIR/nerfacc/issues/207#issuecomment-1653621720
  - https://github.com/ashawkey/torch-ngp/issues/176
  
  Args:
      positions (torch.Tensor): Input positions tensor with shape [B*N, D].
  
  Returns:
      Tuple[torch.Tensor, torch.Tensor]: Tuple containing density and features tensors.
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code: def forward_density_features(
      self, positions: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """
      Calculate both density and features based on the input positions.
  
      This function takes into account edge cases like empty input tensors and calculates
      the density and features accordingly. See GitHub issues for details:
      - https://github.com/KAIR-BAIR/nerfacc/issues/207#issuecomment-1653621720
      - https://github.com/ashawkey/torch-ngp/issues/176
  
      Args:
          positions (torch.Tensor): Input positions tensor with shape [B*N, D].
  
      Returns:
          Tuple[torch.Tensor, torch.Tensor]: Tuple containing density and features tensors.
      """
  
      # Handle empty positions
      if positions.shape[0] == 0:
          sigma = torch.zeros(0, device=positions.device)
          features = torch.zeros(
              0, self.num_input_dims, device=positions.device
          )
          return sigma, features
  
      # Encode positions
      positions_encoding = self.encode(positions)
  
      # Compute density
      density = self.forward_density(positions, positions_encoding)
  
      # Compute features
      features = self.forward_features(positions, positions_encoding)
  
      return density, features
  
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 6 for calling the NeRFBase obj's underlying member method code with example usage: obj.forward_density_features(positions) and returns: density,  features, sigma,  features