code: def test_linear(
      self,
      N: int,
      Z: int,
      H: int,
      W: int,
      input_dim: int,
      bias: bool,
      batch_first: bool,
      grad_sample_mode: str,
  ):
      if input_dim == 2:
          if not batch_first:
              return  # see https://github.com/pytorch/opacus/pull/265
          else:
              x_shape = [N, W]
      if input_dim == 3:
          x_shape = [N, Z, W]
      if input_dim == 4:
          x_shape = [N, Z, H, W]
  
      linear = nn.Linear(W, W + 2, bias=bias)
      x = torch.randn(x_shape)
      if not batch_first:
          x = x.transpose(0, 1)
      ew_compatible = N > 0
  
      if not ew_compatible and grad_sample_mode == "ew":
          return
  
      self.per_sample_grads_utils_test(
          x, linear, grad_sample_mode, N == 0
      )
  
file_path: opacus/opacus/tests/per_sample_gradients_utils_test.py
imports: import unittest
  from typing import Callable
  import hypothesis.strategies
  import torch
  from hypothesis import given, settings
  from opacus.utils.per_sample_gradients_utils import check_per_sample_gradients_are_correct, get_grad_sample_modes
  from torch import nn
  from .grad_samples.common import expander, shrinker
label_desc: Use image node_id 3 for calling the PerSampleGradientsUtilsTest obj's underlying member method code with example usage: obj.test_linear(N, Z, H, W, input_dim, bias, batch_first, grad_sample_mode) without return types