code: def get_input_transform_type() -> Type[InputTransform]:
      return Normalize
  
imports: from logging import Logger
  from typing import Any, Dict, List, Optional, Type
  import numpy
  from ax.core.base_trial import TrialStatus
  from ax.core.experiment import Experiment
  from ax.core.observation import Observation, ObservationData, ObservationFeatures
  from ax.core.optimization_config import OptimizationConfig
  from ax.core.parameter import FixedParameter, RangeParameter
  from ax.core.search_space import SearchSpace
  from ax.modelbridge.base import ModelBridge
  from ax.modelbridge.dispatch_utils import choose_generation_strategy
  from ax.modelbridge.factory import get_sobol
  from ax.modelbridge.generation_node import GenerationNode
  from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
  from ax.modelbridge.model_spec import ModelSpec
  from ax.modelbridge.registry import Models
  from ax.modelbridge.transforms.base import Transform
  from ax.modelbridge.transforms.int_to_float import IntToFloat
  from ax.modelbridge.transition_criterion import MaxGenerationParallelism, MaxTrials, MinimumPreferenceOccurances, MinTrials
  from ax.models.torch.botorch_modular.surrogate import Surrogate
  from ax.utils.common.logger import get_logger
  from ax.utils.testing.core_stubs import get_experiment, get_search_space, get_search_space_for_value
  from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement
  from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
  from botorch.models.transforms.input import InputTransform, Normalize
  from botorch.models.transforms.outcome import OutcomeTransform, Standardize
label_desc: Use image node_id 12 for calling a global function with example usage: get_input_transform_type() and returns: Normalize