code: def test_abstract_base_input_transform(self) -> None:
      with self.assertRaises(TypeError):
          InputTransform()
      X = torch.zeros([1])
      X_tf = torch.ones([1])
      # test transform_on_train and transform_on_eval
      ipt = NotSoAbstractInputTransform(
          transform_on_train=True, transform_on_eval=True
      )
      self.assertTrue(torch.equal(ipt(X), X_tf))
      ipt.eval()
      self.assertTrue(torch.equal(ipt(X), X_tf))
      ipt = NotSoAbstractInputTransform(
          transform_on_train=True, transform_on_eval=False
      )
      self.assertTrue(torch.equal(ipt(X), X_tf))
      ipt.eval()
      self.assertTrue(torch.equal(ipt(X), X))
      ipt = NotSoAbstractInputTransform(
          transform_on_train=False, transform_on_eval=True
      )
      self.assertTrue(torch.equal(ipt(X), X))
      ipt.eval()
      self.assertTrue(torch.equal(ipt(X), X_tf))
      ipt = NotSoAbstractInputTransform(
          transform_on_train=False, transform_on_eval=False
      )
      self.assertTrue(torch.equal(ipt(X), X))
      ipt.eval()
      self.assertTrue(torch.equal(ipt(X), X))
  
      # test equals
      ipt2 = NotSoAbstractInputTransform(
          transform_on_train=False, transform_on_eval=False
      )
      self.assertTrue(ipt.equals(ipt2))
      ipt3 = NotSoAbstractInputTransform(
          transform_on_train=True, transform_on_eval=False
      )
      self.assertFalse(ipt.equals(ipt3))
  
      with self.assertRaises(NotImplementedError):
          ipt.untransform(None)
  
      # test preprocess_transform
      ipt4 = NotSoAbstractInputTransform(
          transform_on_train=True,
          transform_on_eval=False,
      )
      self.assertTrue(torch.equal(ipt4.preprocess_transform(X), X_tf))
      ipt4.transform_on_train = False
      self.assertTrue(torch.equal(ipt4.preprocess_transform(X), X))
  
      # test transform_on_fantasize
      ipt5 = NotSoAbstractInputTransform(
          transform_on_train=True,
          transform_on_eval=True,
          transform_on_fantasize=True,
      )
      ipt5.eval()
      self.assertTrue(torch.equal(ipt5(X), X_tf))
      with fantasize():
          self.assertTrue(torch.equal(ipt5(X), X_tf))
      ipt5.transform_on_fantasize = False
      self.assertTrue(torch.equal(ipt5(X), X_tf))
      with fantasize():
          self.assertTrue(torch.equal(ipt5(X), X))
  
      # testing one line of AffineInputTransform
      # that doesn't have coverage otherwise
      d = 3
      coefficient, offset = torch.ones(d), torch.zeros(d)
      affine = AffineInputTransform(d, coefficient, offset)
      X = torch.randn(2, d)
      with self.assertRaises(NotImplementedError):
          affine._update_coefficients(X)
  
imports: import itertools
  import warnings
  from copy import deepcopy
  from random import randint
  import torch
  from botorch import settings
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.exceptions.warnings import UserInputWarning
  from botorch.models.transforms.input import AffineInputTransform, AppendFeatures, ChainedInputTransform, FilterFeatures, InputPerturbation, InputStandardize, InputTransform, Log10, Normalize, OneHotToNumeric, Round, Warp
  from botorch.models.transforms.utils import expand_and_copy_tensor
  from botorch.models.utils import fantasize
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.priors import LogNormalPrior
  from torch import Tensor
  from torch.distributions import Kumaraswamy
  from torch.nn import Module
  from torch.nn.functional import one_hot
label_desc: Use image node_id 1 for calling the TestInputTransforms obj's underlying member method code with example usage: obj.test_abstract_base_input_transform() without return types