code: def training_step(self, batch, batch_idx):
      # experiment iterations ratio
      # i.e. what proportion of this experiment have we completed (in terms of iterations) so far?
      exp_iter_ratio = self.global_step / self.iters
  
      # TODO(ahmadki): move to database
      if exp_iter_ratio < self.latent_iter_ratio:
          ambient_ratio = 1.0
          shading_type = ShadingEnum.NORMAL
          as_latent = True
      else:
          if exp_iter_ratio <= self.albedo_iter_ratio:
              ambient_ratio = 1.0
              shading_type = None
          else:
              # random shading
              ambient_ratio = (
                  self.min_ambient_ratio
                  + (1.0 - self.min_ambient_ratio) * random.random()
              )
              rand = random.random()
              if rand >= (1.0 - self.textureless_ratio):
                  shading_type = ShadingEnum.TEXTURELESS
              else:
                  shading_type = ShadingEnum.LAMBERTIAN
  
          as_latent = False
  
      return_normal_image = bool(self.lambda_2d_normal_smooth)
      return_normal_perturb = bool(self.lambda_3d_normal_smooth)
      return_vertices = bool(self.lambda_mesh_laplacian)
      return_faces = bool(self.lambda_mesh_normal) or bool(
          self.lambda_mesh_laplacian
      )
      return_faces_normals = bool(self.lambda_mesh_normal)
      outputs = self(
          rays_o=batch["rays_o"],  # [B, H, W, 3]
          rays_d=batch["rays_d"],  # [B, H, W, 3]
          mvp=batch["mvp"],  # [B, 4, 4]
          perturb=True,
          ambient_ratio=ambient_ratio,
          shading_type=shading_type,
          binarize=False,
          return_normal_image=return_normal_image,
          return_normal_perturb=return_normal_perturb,
          return_vertices=return_vertices,
          return_faces=return_faces,
          return_faces_normals=return_faces_normals,
      )
  
      if as_latent:
          pred_rgb = (
              torch.cat([outputs["image"], outputs["opacity"]], dim=-1)
              .permute(0, 3, 1, 2)
              .contiguous()
          )  # [B, 4, H, W]
      else:
          pred_rgb = (
              outputs["image"].permute(0, 3, 1, 2).contiguous()
          )  # [B, 3, H, W]
  
      # TODO(ahmadki): move into guidance
      azimuth = batch["azimuth"]
      text_z = [self.text_z["uncond"]] * azimuth.shape[0]
      for b in range(azimuth.shape[0]):
          if azimuth[b] >= -90 and azimuth[b] < 90:
              if azimuth[b] >= 0:
                  r = 1 - azimuth[b] / 90
              else:
                  r = 1 + azimuth[b] / 90
              start_z = self.text_z["front"]
              end_z = self.text_z["side"]
          else:
              if azimuth[b] >= 0:
                  r = 1 - (azimuth[b] - 90) / 90
              else:
                  r = 1 + (azimuth[b] + 90) / 90
              start_z = self.text_z["side"]
              end_z = self.text_z["back"]
          pos_z = r * start_z + (1 - r) * end_z
          text_z.append(pos_z)
      text_z = torch.cat(text_z, dim=0)
  
      loss_dict = {}
  
      # SDS loss
      guidance_loss = self.guidance.train_step(
          text_z,
          pred_rgb,
          as_latent=as_latent,
          guidance_scale=self.guidance_scale,
      )
      loss_dict["lambda_sds"] = guidance_loss * self.lambda_sds
  
      # opacity loss
      if self.lambda_opacity > 0 and "opacity" in outputs:
          loss_opacity = (outputs["opacity"] ** 2).mean()
          loss_dict["loss_opacity"] = self.lambda_opacity * loss_opacity
  
      # entropy loss
      if self.lambda_entropy > 0 and "weights" in outputs:
          alphas = outputs["weights"].clamp(1e-5, 1 - 1e-5)
          loss_entropy = (
              -alphas * torch.log2(alphas)
              - (1 - alphas) * torch.log2(1 - alphas)
          ).mean()
          lambda_entropy = self.lambda_entropy * min(
              1, 2 * self.global_step / self.iters
          )
          loss_dict["loss_entropy"] = lambda_entropy * loss_entropy
  
      if self.lambda_2d_normal_smooth > 0 and "normal_image" in outputs:
          pred_normal = outputs["normal_image"]
          loss_smooth = (
              pred_normal[:, 1:, :, :] - pred_normal[:, :-1, :, :]
          ).square().mean() + (
              pred_normal[:, :, 1:, :] - pred_normal[:, :, :-1, :]
          ).square().mean()
          loss_dict["loss_smooth"] = (
              self.lambda_2d_normal_smooth * loss_smooth
          )
  
      # orientation loss
      if self.lambda_orientation > 0 and all(
          key in outputs for key in ["weights", "normals", "dirs"]
      ):
          loss_orientation = (
              outputs["weights"].detach()
              * (outputs["normals"] * outputs["dirs"])
              .sum(-1)
              .clamp(min=0)
              ** 2
          )
          loss_orientation = loss_orientation.mean()
          loss_dict["loss_orientation"] = (
              self.lambda_orientation * loss_orientation
          )
  
      if self.lambda_3d_normal_smooth > 0 and all(
          key in outputs for key in ["normals", "normal_perturb"]
      ):
          loss_normal_perturb = (
              (outputs["normal_perturb"] - outputs["normals"])
              .abs()
              .mean()
          )
          loss_dict["loss_normal_smooth"] = (
              self.lambda_3d_normal_smooth * loss_normal_perturb
          )
  
      if self.lambda_mesh_normal > 0 and all(
          key in outputs for key in ["face_normals", "faces"]
      ):
          normal_consistency_loss = self.normal_consistency_loss_fn(
              face_normals=outputs["face_normals"],
              t_pos_idx=outputs["faces"],
          )
          loss_dict["normal_consistency_loss"] = (
              self.lambda_mesh_normal * normal_consistency_loss
          )
  
      if self.lambda_mesh_laplacian > 0 and all(
          key in outputs for key in ["verts", "faces"]
      ):
          laplacian_loss = self.laplacian_smooth_loss_fn(
              verts=outputs["verts"], faces=outputs["faces"]
          )
          loss_dict["laplacian_loss"] = (
              self.lambda_mesh_laplacian * laplacian_loss
          )
  
      loss = sum(loss_dict.values())
  
      self.log_dict(loss_dict, prog_bar=False, rank_zero_only=True)
      self.log("loss", loss, prog_bar=True, rank_zero_only=True)
  
      # TODO(ahmadki): LearningRateMonitor
      lr = self._optimizer.param_groups[0]["lr"]
      self.log("lr", lr, prog_bar=True, rank_zero_only=True)
  
      self.log(
          "global_step",
          self.global_step + 1,
          prog_bar=True,
          rank_zero_only=True,
      )
  
      return loss
  
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 2 for calling the DreamFusion obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: loss