code: def get_x_shape_and_norm_shape(H, N, W, Z, input_dim, norm_dim):
      if norm_dim == 1:
          normalized_shape = W
          if input_dim == 2:
              x_shape = [N, W]
          if input_dim == 3:
              x_shape = [N, Z, W]
          if input_dim == 4:
              x_shape = [N, Z, H, W]
      elif norm_dim == 2:
          if input_dim == 3:
              normalized_shape = [Z, W]
              x_shape = [N, Z, W]
          if input_dim == 4:
              normalized_shape = [H, W]
              x_shape = [N, Z, H, W]
      elif norm_dim == 3:
          normalized_shape = [Z, H, W]
          x_shape = [N, Z, H, W]
      return normalized_shape, x_shape
  
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 2 for calling the LayerNorm_test obj's underlying member method code with example usage: obj.get_x_shape_and_norm_shape(H, N, W, Z, input_dim, norm_dim) and returns: normalized_shape,  x_shape