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: Generate a mesh from the nerf.
  
  Args:
      resolution (Optional[int]): Resolution of the mesh grid. Default is 128.
      batch_size (int): Batch size for the mesh generation. Default is 128.
      density_thresh (Optional[float]): Density threshold for the mesh generation. Default is None, will be calculated from mean density.
  
  Returns:
      trimesh.base.Trimesh: Mesh object.
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code: def mesh(
      self,
      resolution: Optional[int] = 128,
      batch_size: int = 128,
      density_thresh: Optional[float] = None,
  ) -> trimesh.base.Trimesh:
      """
      Generate a mesh from the nerf.
  
      Args:
          resolution (Optional[int]): Resolution of the mesh grid. Default is 128.
          batch_size (int): Batch size for the mesh generation. Default is 128.
          density_thresh (Optional[float]): Density threshold for the mesh generation. Default is None, will be calculated from mean density.
  
      Returns:
          trimesh.base.Trimesh: Mesh object.
      """
      # Generate a grid of 3D points
      x = np.linspace(-self.bound, self.bound, resolution)
      y = np.linspace(-self.bound, self.bound, resolution)
      z = np.linspace(-self.bound, self.bound, resolution)
      xx, yy, zz = np.meshgrid(x, y, z)
  
      grid = np.stack(
          (xx, yy, zz), axis=-1
      )  # Shape (resolution, resolution, resolution, 3)
      torch_grid = (
          torch.tensor(grid, dtype=torch.float32)
          .reshape(-1, 3)
          .to(device="cuda")
      )
  
      def batch_process(fn, input, batch_size):
          num_points = input.shape[0]
          batches = [
              input[i : i + batch_size]
              for i in range(0, num_points, batch_size)
          ]
          results = [fn(batch) for batch in batches]
          results = [
              result.detach().cpu().numpy() for result in results
          ]
          return np.concatenate(results, axis=0)
  
      density = batch_process(
          fn=self.forward_density,
          input=torch_grid,
          batch_size=batch_size,
      )
      density = density.reshape(resolution, resolution, resolution)
  
      # If not provided set density_thresh based on mean density
      if density_thresh is None:
          density_thresh = density[density > 1e-3].mean().item()
  
      # Apply Marching Cubes
      vertices, triangles = mcubes.marching_cubes(
          density, density_thresh
      )
  
      # Create a new Mesh
      mesh = trimesh.Trimesh(vertices=vertices, faces=triangles)
  
      # Basic mesh cleaning and optimization
      mesh.remove_unreferenced_vertices()
      mesh.remove_infinite_values()
      mesh.remove_duplicate_faces()
  
      # Scale vertices back to [-self.bound, self.bound]
      scaled_vertices = (
          -self.bound + (mesh.vertices / resolution) * 2 * self.bound
      )
      mesh.vertices = scaled_vertices
  
      # Assigning color to vertices
      scaled_vertices_torch = torch.tensor(
          scaled_vertices, dtype=torch.float32
      ).to(device="cuda")
      color = batch_process(
          fn=self.forward_features,
          input=scaled_vertices_torch,
          batch_size=batch_size,
      )
      color = (color * 255).astype(np.uint8)
      mesh.visual.vertex_colors = color
  
      return mesh
  
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 11 for calling the NeRFBase obj's underlying member method code with example usage: obj.mesh(resolution, batch_size, density_thresh) and returns: mesh, np