code: def test_accumulated_gradient(self):
      params = load_model_config(
          "segmentation", "pointnet2", "pointnet2ms"
      )
      config_training = OmegaConf.load(
          os.path.join(DIR, "test_config/training_config.yaml")
      )
      dataset = MockDatasetGeometric(5)
      model = instantiate_model(params, dataset)
      model.instantiate_optimizers(config_training)
      model.set_input(dataset[0], "cpu")
      expected_make_optimizer_step = [
          False,
          False,
          True,
          False,
          False,
          True,
          False,
          False,
          True,
          False,
      ]
      expected_contains_grads = [
          False,
          True,
          True,
          False,
          True,
          True,
          False,
          True,
          True,
          False,
      ]
      make_optimizer_steps = []
      contains_grads = []
      for epoch in range(10):
          model.forward()
  
          make_optimizer_step = (
              model._manage_optimizer_zero_grad()
          )  # Accumulate gradient if option is up
          make_optimizer_steps.append(make_optimizer_step)
          grad_ = model._modules["lin1"].weight.grad
          if grad_ is not None:
              contains_grads.append((grad_.sum() != 0).item())
          else:
              contains_grads.append(False)
  
          model.backward()  # calculate gradients
  
          if make_optimizer_step:
              model._optimizer.step()  # update parameters
  
      self.assertEqual(contains_grads, expected_contains_grads)
      self.assertEqual(
          make_optimizer_steps, expected_make_optimizer_step
      )
  
imports: import unittest
  import torch
  import omegaconf
  from omegaconf import OmegaConf, DictConfig
  from torch.nn import Sequential, Linear, ReLU, LeakyReLU, BatchNorm1d, Dropout
  import os
  import sys
  from torch_geometric.data import Data
  from torch_points3d.models.base_model import BaseModel
  from test.mockdatasets import MockDatasetGeometric
  from torch_points3d.models.model_factory import instantiate_model
label_desc: Use image node_id 4 for calling the TestBaseModel obj's underlying member method code with example usage: obj.test_accumulated_gradient() without return types