code: def test_log10_transform(self) -> None:
      # set seed to range where this is known to not be flaky
      torch.manual_seed(randint(0, 1000))
      for dtype in (torch.float, torch.double):
          # basic init
          indices = [0, 2]
          log_tf = Log10(indices=indices)
          self.assertEqual(log_tf.indices.tolist(), indices)
          self.assertTrue(log_tf.training)
  
          # basic usage
          for batch_shape in (torch.Size(), torch.Size([3])):
              X = 1 + 5 * torch.rand(
                  *batch_shape, 4, 3, device=self.device, dtype=dtype
              )
              log_tf = Log10(indices=indices)
              X_tf = log_tf(X)
              expected_X_tf = X.clone()
              expected_X_tf[..., indices] = expected_X_tf[
                  ..., indices
              ].log10()
              # check non-integers parameters are unchanged
              self.assertTrue(torch.equal(expected_X_tf, X_tf))
              untransformed_X = log_tf.untransform(X_tf)
              self.assertAllClose(untransformed_X, X)
  
              # test no transform on eval
              log_tf = Log10(indices=indices, transform_on_eval=False)
              X_tf = log_tf(X)
              self.assertFalse(torch.equal(X, X_tf))
              log_tf.eval()
              X_tf = log_tf(X)
              self.assertTrue(torch.equal(X, X_tf))
  
              # test no transform on train
              log_tf = Log10(indices=indices, transform_on_train=False)
              X_tf = log_tf(X)
              self.assertTrue(torch.equal(X, X_tf))
              log_tf.eval()
              X_tf = log_tf(X)
              self.assertFalse(torch.equal(X, X_tf))
  
              # test equals
              log_tf2 = Log10(indices=indices, transform_on_train=False)
              self.assertTrue(log_tf.equals(log_tf2))
              # test different transform_on_train
              log_tf2 = Log10(indices=indices)
              self.assertFalse(log_tf.equals(log_tf2))
              # test different indices
              log_tf2 = Log10(indices=[0, 1], transform_on_train=False)
              self.assertFalse(log_tf.equals(log_tf2))
  
              # test preprocess_transform
              log_tf.transform_on_train = False
              self.assertTrue(
                  torch.equal(log_tf.preprocess_transform(X), X)
              )
              log_tf.transform_on_train = True
              self.assertTrue(
                  torch.equal(log_tf.preprocess_transform(X), X_tf)
              )
  
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 7 for calling the TestInputTransforms obj's underlying member method code with example usage: obj.test_log10_transform() without return types