code: def _assert_n_samples_batched_size(
      self,
      type: str,
      approximation_method: str = "gausslegendre",
      nt_samples_batch_size: int = None,
  ) -> None:
      model = BasicModel_MultiLayer()
      input = (
          torch.tensor(
              [[1.5, 2.0, 1.3], [0.5, 0.1, 2.3], [1.5, 2.0, 1.3]],
              requires_grad=True,
          ),
      )
      self._compute_attribution_and_evaluate(
          model,
          input,
          type=type,
          target=0,
          nt_samples_batch_size=nt_samples_batch_size,
          approximation_method=approximation_method,
      )
  
imports: import unittest
  from typing import Any, cast, Tuple, Union
  import torch
  from captum._utils.common import _zeros
  from captum._utils.typing import BaselineType, Tensor, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._utils.common import _tensorize_baseline
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel, BasicModel2, BasicModel3, BasicModel4_MultiArgs, BasicModel5_MultiArgs, BasicModel6_MultiTensor, BasicModel_MultiLayer
  from torch.nn import Module
label_desc: Use image node_id 39 for calling the Test obj's underlying member method code with example usage: obj._assert_n_samples_batched_size(type, approximation_method, nt_samples_batch_size) without return types