code: def _shared_predict(self, data):
      outputs = self(
          rays_o=data["rays_o"],  # [B, H, W, 3]
          rays_d=data["rays_d"],  # [B, H, W, 3]
          mvp=data["mvp"],
          perturb=False,
          ambient_ratio=data["ambient_ratio"]
          if "ambient_ratio" in data
          else 1.0,  # TODO(ahmadki): move to dataset
          shading_type=data["shading_type"]
          if "shading_type" in data
          else None,  # TODO(ahmadki): move to dataset
          binarize=False,
          return_normal_image=False,
          return_normal_perturb=False,
          return_vertices=False,
          return_faces=False,
          return_faces_normals=False,
      )
  
      images_np = outputs["image"].detach().cpu().numpy()
      images_np = (images_np * 255).astype(np.uint8)
  
      depths_np = outputs["depth"].detach().cpu().numpy()
      depths_np = (depths_np - depths_np.min()) / (
          np.ptp(depths_np) + 1e-6
      )
      depths_np = (depths_np * 255).astype(np.uint8)
  
      return images_np, depths_np
  
imports: import os
  import random
  import cv2
  import imageio
  import numpy
  import torch
  from nemo.collections.multimodal.models.nerf.txt2nerf_base import Txt2NerfBase
  from nemo.collections.multimodal.modules.nerf.loss.laplacian_smooth_loss import LaplacianSmoothLoss
  from nemo.collections.multimodal.modules.nerf.loss.normal_consistency_loss import NormalConsistencyLoss
  from nemo.collections.multimodal.modules.nerf.materials.materials_base import ShadingEnum
  from nemo.core import optim
label_desc: Use image node_id 8 for calling the DreamFusion obj's underlying member method code with example usage: obj._shared_predict(data) and returns: images_np,  depths_np