code: def test_input_bias(
      self,
      N: int,
      Z: int,
      W: int,
      H: int,
      input_dim: int,
      bias: bool,
      batch_first: bool,
  ):
      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)
      self.run_test(
          x, linear, batch_first=batch_first, ew_compatible=N > 0
      )
  
file_path: opacus/opacus/tests/grad_samples/linear_test.py
imports: import hypothesis.strategies
  import torch
  import torch.nn
  from hypothesis import given, settings
  from .common import GradSampleHooks_test
label_desc: Use image node_id 1 for calling the Linear_test obj's underlying member method code with example usage: obj.test_input_bias(N, Z, W, H, input_dim, bias, batch_first) without return types