code: def generate_all_input_matrix(self) -> torch.Tensor:
      A_B_ABi_list = [self.A, self.B]
      for i in range(self.dim):
          AB_i = deepcopy(self.A)
          AB_i[:, i] = self.B[:, i]
          A_B_ABi_list.append(AB_i)
      if self.second_order:
          for i in range(self.dim):
              BA_i = deepcopy(self.B)
              BA_i[:, i] = self.A[:, i]
              A_B_ABi_list.append(BA_i)
      A_B_ABi = torch.cat(A_B_ABi_list, dim=0)
      return A_B_ABi
  
imports: from copy import deepcopy
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import torch
  from ax.modelbridge.torch import TorchModelBridge
  from ax.models.torch.botorch import BotorchModel
  from ax.models.torch.botorch_modular.model import BoTorchModel
  from ax.utils.common.typeutils import checked_cast
  from botorch.models.model import Model, ModelList
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.sampling import draw_sobol_samples
  from botorch.utils.transforms import unnormalize
  from torch import Tensor
label_desc: Use image node_id 2 for calling the SobolSensitivity obj's underlying member method code with example usage: obj.generate_all_input_matrix() and returns: A_B_ABi