code: def _one_model_test(
      self,
      model,
      device,
      n_train_cameras: int = 5,
      eval_test: bool = True,
      bw_test: bool = True,
  ):
      R, T = look_at_view_transform(
          azim=torch.rand(n_train_cameras) * 360
      )
      cameras = PerspectiveCameras(R=R, T=T, device=device)
  
      N, H, W = (
          n_train_cameras,
          model.render_image_height,
          model.render_image_width,
      )
  
      random_args = {
          "camera": cameras,
          "fg_probability": _random_input_tensor(
              N, 1, H, W, True, device
          ),
          "depth_map": _random_input_tensor(N, 1, H, W, False, device)
          + 0.1,
          "mask_crop": _random_input_tensor(N, 1, H, W, True, device),
          "sequence_name": ["sequence"] * N,
          "image_rgb": _random_input_tensor(N, 3, H, W, False, device),
      }
  
      # training foward pass
      model.train()
      train_preds = model(
          **random_args,
          evaluation_mode=EvaluationMode.TRAINING,
      )
      self.assertTrue(
          train_preds["objective"].isfinite().item()
      )  # check finiteness of the objective
  
      if bw_test:
          train_preds["objective"].backward()
  
      if eval_test:
          model.eval()
          with torch.no_grad():
              eval_preds = model(
                  **random_args,
                  evaluation_mode=EvaluationMode.EVALUATION,
              )
              self.assertEqual(
                  eval_preds["images_render"].shape,
                  (
                      1,
                      3,
                      model.render_image_height,
                      model.render_image_width,
                  ),
              )
  
imports: import os
  import unittest
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch3d.implicitron.models.generic_model import GenericModel
  from pytorch3d.implicitron.models.renderer.base import EvaluationMode
  from pytorch3d.implicitron.tools.config import expand_args_fields, get_default_args
  from pytorch3d.renderer.cameras import look_at_view_transform, PerspectiveCameras
  from tests.common_testing import get_pytorch3d_dir
  from .common_resources import provide_resnet34
label_desc: Use image node_id 5 for calling the TestGenericModel obj's underlying member method code with example usage: obj._one_model_test(model, device, n_train_cameras, eval_test, bw_test) without return types