code: def __init__(
      self,
      bound,
      update_interval,
      grid_resolution,
      density_thresh,
      max_steps,
      dt_gamma,
  ):
      super().__init__(bound, update_interval)
  
      self.cascade = 1 + math.ceil(math.log2(bound))
      self.grid_resolution = grid_resolution
      self.density_thresh = density_thresh
      self.dt_gamma = dt_gamma
      self.max_steps = max_steps
  
      # density grid
      # TODO(ahmadki): needs rework
      density_grid = torch.zeros(
          [self.cascade, self.grid_resolution**3]
      )  # [CAS, H * H * H]
      density_bitfield = torch.zeros(
          self.cascade * self.grid_resolution**3 // 8,
          dtype=torch.uint8,
      )  # [CAS * H * H * H // 8]
      self.register_buffer("density_grid", density_grid)
      self.register_buffer("density_bitfield", density_bitfield)
      self.mean_density = 0
      self.iter_density = 0
  
      # TODO(ahmadki): needs rework
      self.nerf = None
      self.material = None
      self.background = None
  
file_path: NeMo/nemo/collections/multimodal/modules/nerf/renderers/torchngp_volume_renderer.py
imports: import math
  import torch
  import torch.nn.functional
  import nemo.collections.multimodal.modules.nerf.utils.torch_ngp.raymarching
  from nemo.collections.multimodal.modules.nerf.materials.materials_base import ShadingEnum
  from nemo.collections.multimodal.modules.nerf.renderers.base_renderer import BaseRenderer
label_desc: Use image node_id 1 to create a new TorchNGPVolumeRenderer object from inherited base classes: BaseRenderer with example: obj = TorchNGPVolumeRenderer(bound, update_interval, grid_resolution, density_thresh, max_steps, dt_gamma)