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 normals using finite differences.
  
  Args:
      positions (torch.Tensor): Input positions tensor with shape [B*N, D].
      eps (float): A small value for finite difference calculation. Default is 1e-2.
  
  Returns:
      torch.Tensor: Calculated normals tensor [B*N, D]
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code: def normal_finite_differences(
      self, positions: torch.Tensor, eps: float = 1e-2
  ) -> torch.Tensor:
      """
      Calculate normals using finite differences.
  
      Args:
          positions (torch.Tensor): Input positions tensor with shape [B*N, D].
          eps (float): A small value for finite difference calculation. Default is 1e-2.
  
      Returns:
          torch.Tensor: Calculated normals tensor [B*N, D]
      """
      # Create perturbation tensor
      perturb = (
          torch.eye(self.num_input_dims).to(positions.device).float()
          * eps
      )  # Shape (D, D)
  
      # Expand dims for batched operation
      positions_expanded = positions[:, None, :]  # (B*N, 1, D)
      perturb_expanded = perturb[None, :, :]  # (1, D, D)
  
      # Compute perturbed points
      if self.normal_type == NormalTypeEnum.FORWARD_FINITE_DIFFERENCE:
          positions_perturbed = (
              positions_expanded + perturb_expanded
          )  # (B*N, D, D)
      elif (
          self.normal_type == NormalTypeEnum.BACKWARD_FINITE_DIFFERENCE
      ):
          positions_perturbed = (
              positions_expanded - perturb_expanded
          )  # (B*N, D, D)
      elif self.normal_type == NormalTypeEnum.CENTRAL_FINITE_DIFFERENCE:
          positions_perturbed_pos = (
              positions_expanded + perturb_expanded
          )  # (B*N, D, D)
          positions_perturbed_neg = (
              positions_expanded - perturb_expanded
          )  # (B*N, D, D)
          positions_perturbed = torch.cat(
              [positions_perturbed_pos, positions_perturbed_neg], dim=1
          )  # (B*N, 2*D, D)
  
      # Reshape perturbed points for batched function call
      positions_perturbed_reshaped = positions_perturbed.view(
          -1, self.num_input_dims
      )  # (B*N * {D or 2*D}, D)
  
      # Evaluate function at perturbed points
      perturbed_sigma = self.forward_density(
          positions_perturbed_reshaped
      )  # (B*N * {D or 2*D}, 1)
  
      # Reshape function values
      if self.normal_type == NormalTypeEnum.CENTRAL_FINITE_DIFFERENCE:
          perturbed_sigma = perturbed_sigma.view(
              -1, 2 * self.num_input_dims
          )  # (B*N, 2*D)
          sigma_pos, sigma_neg = torch.chunk(
              perturbed_sigma, 2, dim=1
          )  # (B*N, D) each
          normal = 0.5 * (sigma_pos - sigma_neg) / eps  # (B*N, D)
      else:
          perturbed_sigma = perturbed_sigma.view(
              -1, self.num_input_dims
          )  # (B*N, D)
          sigma = self.forward_density(
              positions
          )  # (B*N,) # TODO(ahmadki): use the value from forward ?
          if (
              self.normal_type
              == NormalTypeEnum.FORWARD_FINITE_DIFFERENCE
          ):
              normal = (
                  perturbed_sigma - sigma[:, None]
              ) / eps  # (B*N, D)
          else:  # self.normal_type == BACKWARD_FINITE_DIFFERENCE
              normal = (
                  sigma[:, None] - perturbed_sigma
              ) / eps  # (B*N, D)
  
      return -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 10 for calling the NeRFBase obj's underlying member method code with example usage: obj.normal_finite_differences(positions, eps) and returns: unknown