code: def test_input_norm(
      self,
      N: int,
      Z: int,
      W: int,
      H: int,
      input_dim: int,
      norm_dim: int,
  ):
      if norm_dim >= input_dim:
          return
      normalized_shape, x_shape = self.get_x_shape_and_norm_shape(
          H, N, W, Z, input_dim, norm_dim
      )
  
      norm = nn.LayerNorm(normalized_shape, elementwise_affine=True)
      x = torch.randn(x_shape)
      self.run_test(x, norm, batch_first=True)
  
file_path: opacus/opacus/tests/grad_samples/layer_norm_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 LayerNorm_test obj's underlying member method code with example usage: obj.test_input_norm(N, Z, W, H, input_dim, norm_dim) without return types