class_docstr: Sherpa wrapper around the GPyOpt package
  (https://github.com/SheffieldML/GPyOpt).
  
  Args:
      model_type (str): The model used:
          - 'GP', standard Gaussian process.
          - 'GP_MCMC', Gaussian process with prior in the hyper-parameters.
          - 'sparseGP', sparse Gaussian process.
          - 'warperdGP', warped Gaussian process.
          - 'InputWarpedGP', input warped Gaussian process
          - 'RF', random forest (scikit-learn).
      num_initial_data_points (int): Number of data points to collect before
          fitting model. Needs to be greater/equal to the number of hyper-
          parameters that are being optimized. Using default 'infer' corres-
          ponds to number of hyperparameters + 1 or 0 if results are not empty.
      initial_data_points (list[dict] or pandas.Dataframe): Specifies initial
          data points. If len(initial_data_points)<num_initial_data_points
          then the rest is randomly sampled. Use this option to provide
          hyperparameter configurations that are known to be good.
      acquisition_type (str): Type of acquisition function to use.
          - 'EI', expected improvement.
          - 'EI_MCMC', integrated expected improvement (requires GP_MCMC model).
          - 'MPI', maximum probability of improvement.
          - 'MPI_MCMC', maximum probability of improvement (requires GP_MCMC model).
          - 'LCB', GP-Lower confidence bound.
          - 'LCB_MCMC', integrated GP-Lower confidence bound (requires GP_MCMC model).
      max_concurrent (int): The number of concurrent trials. This generates
          a batch of max_concurrent trials from GPyOpt to evaluate. If a new
          observation becomes available, the model is re-evaluated and a new
          batch is created regardless of whether the previous batch was used
          up. The used method is local penalization.
      verbosity (bool): Print models and other options during the optimization.
      max_num_trials (int): maximum number of trials to run for.
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docstr: Turn Sherpa parameter definitions into GPyOpt parameter definitions.
docstr_tok: ["Turn","Sherpa","parameter","definitions","into","GPyOpt","parameter","definitions","."]
code: def _initialize_domain(parameters):
      """
      Turn Sherpa parameter definitions into GPyOpt parameter definitions.
      """
      domain = []
      for p in parameters:
          domain.append(
              ParameterTransform.from_parameter(p).to_gpyopt_domain()
          )
      return domain
  
imports: import numpy
  import logging
  import sherpa
  from sherpa.algorithms import Algorithm
  import pandas
  from sherpa.core import Choice, Continuous, Discrete, Ordinal
  import collections
  import GPyOpt
  import GPy
  import warnings
label_desc: Use image node_id 10 for calling the GPyOpt obj's underlying member method code with example usage: obj._initialize_domain(parameters) and returns: domain