code: def decode_to_image(
      self,
      latents,
      device,
      size: int = 64,
      ray_batch_size: int = 4096,
      n_coarse_samples=64,
      n_fine_samples=128,
  ):
      # project the parameters from the generated latents
      projected_params = self.params_proj(latents)
  
      # update the mlp layers of the renderer
      for name, param in self.mlp.state_dict().items():
          if f"nerstf.{name}" in projected_params.keys():
              param.copy_(projected_params[f"nerstf.{name}"].squeeze(0))
  
      # create cameras object
      camera = create_pan_cameras(size)
      rays = camera.camera_rays
      rays = rays.to(device)
      n_batches = rays.shape[1] // ray_batch_size
  
      coarse_sampler = StratifiedRaySampler()
  
      images = []
  
      for idx in range(n_batches):
          rays_batch = rays[
              :, idx * ray_batch_size : (idx + 1) * ray_batch_size
          ]
  
          # render rays with coarse, stratified samples.
          _, fine_sampler, coarse_model_out = self.render_rays(
              rays_batch, coarse_sampler, n_coarse_samples
          )
          # Then, render with additional importance-weighted ray samples.
          channels, _, _ = self.render_rays(
              rays_batch,
              fine_sampler,
              n_fine_samples,
              prev_model_out=coarse_model_out,
          )
  
          images.append(channels)
  
      images = torch.cat(images, dim=1)
      images = images.view(
          *camera.shape, camera.height, camera.width, -1
      ).squeeze(0)
  
      return images
  
imports: import math
  from dataclasses import dataclass
  from typing import Dict, Optional, Tuple
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  from ...utils import BaseOutput
  from .camera import create_pan_cameras
label_desc: Use image node_id 3 for calling the ShapERenderer obj's underlying member method code with example usage: obj.decode_to_image(latents, device, size, ray_batch_size, n_coarse_samples, n_fine_samples) and returns: images