code: def decode_to_mesh(
      self,
      latents,
      device,
      grid_size: int = 128,
      query_batch_size: int = 4096,
      texture_channels: Tuple = ("R", "G", "B"),
  ):
      # 1. project the the paramters from the generated latents
      projected_params = self.params_proj(latents)
  
      # 2. 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))
  
      # 3. decoding with STF rendering
      # 3.1 query the SDF values at vertices along a regular 128**3 grid
  
      query_points = volume_query_points(self.volume, grid_size)
      query_positions = (
          query_points[None]
          .repeat(1, 1, 1)
          .to(device=device, dtype=self.mlp.dtype)
      )
  
      fields = []
  
      for idx in range(0, query_positions.shape[1], query_batch_size):
          query_batch = query_positions[:, idx : idx + query_batch_size]
  
          model_out = self.mlp(
              position=query_batch,
              direction=None,
              ts=None,
              nerf_level="fine",
              rendering_mode="stf",
          )
          fields.append(model_out.signed_distance)
  
      # predicted SDF values
      fields = torch.cat(fields, dim=1)
      fields = fields.float()
  
      assert (
          len(fields.shape) == 3 and fields.shape[-1] == 1
      ), f"expected [meta_batch x inner_batch] SDF results, but got {fields.shape}"
  
      fields = fields.reshape(1, *([grid_size] * 3))
  
      # create grid 128 x 128 x 128
      # - force a negative border around the SDFs to close off all the models.
      full_grid = torch.zeros(
          1,
          grid_size + 2,
          grid_size + 2,
          grid_size + 2,
          device=fields.device,
          dtype=fields.dtype,
      )
      full_grid.fill_(-1.0)
      full_grid[:, 1:-1, 1:-1, 1:-1] = fields
      fields = full_grid
  
      # apply a differentiable implementation of Marching Cubes to construct meshs
      raw_meshes = []
      mesh_mask = []
  
      for field in fields:
          raw_mesh = self.mesh_decoder(
              field,
              self.volume.bbox_min,
              self.volume.bbox_max - self.volume.bbox_min,
          )
          mesh_mask.append(True)
          raw_meshes.append(raw_mesh)
  
      mesh_mask = torch.tensor(mesh_mask, device=fields.device)
      max_vertices = max(len(m.verts) for m in raw_meshes)
  
      # 3.2. query the texture color head at each vertex of the resulting mesh.
      texture_query_positions = torch.stack(
          [
              m.verts[torch.arange(0, max_vertices) % len(m.verts)]
              for m in raw_meshes
          ],
          dim=0,
      )
      texture_query_positions = texture_query_positions.to(
          device=device, dtype=self.mlp.dtype
      )
  
      textures = []
  
      for idx in range(
          0, texture_query_positions.shape[1], query_batch_size
      ):
          query_batch = texture_query_positions[
              :, idx : idx + query_batch_size
          ]
  
          texture_model_out = self.mlp(
              position=query_batch,
              direction=None,
              ts=None,
              nerf_level="fine",
              rendering_mode="stf",
          )
          textures.append(texture_model_out.channels)
  
      # predict texture color
      textures = torch.cat(textures, dim=1)
  
      textures = _convert_srgb_to_linear(textures)
      textures = textures.float()
  
      # 3.3 augument the mesh with texture data
      assert len(textures.shape) == 3 and textures.shape[-1] == len(
          texture_channels
      ), f"expected [meta_batch x inner_batch x texture_channels] field results, but got {textures.shape}"
  
      for m, texture in zip(raw_meshes, textures):
          texture = texture[: len(m.verts)]
          m.vertex_channels = dict(
              zip(texture_channels, texture.unbind(-1))
          )
  
      return raw_meshes[0]
  
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 4 for calling the ShapERenderer obj's underlying member method code with example usage: obj.decode_to_mesh(latents, device, grid_size, query_batch_size, texture_channels) and returns: raw_meshes