code: def get_init_and_post_gp(
      fidel_dim, domain_dim, num_data, kernel_scale, dim_bw_power=0.5
  ):
      """Generates a GP and data, constructs posterior and returns everything."""
      # pylint: disable=too-many-locals
      kernel_bw_scaling = float(fidel_dim + domain_dim) ** dim_bw_power
      fidel_kernel = kernel.SEKernel(
          fidel_dim,
          1,
          (1 + np.random.random(fidel_dim)) * kernel_bw_scaling,
      )
      domain_kernel = kernel.SEKernel(
          domain_dim,
          1,
          (0.1 + 0.2 * np.random.random(domain_dim))
          * kernel_bw_scaling,
      )
      mean_func_const_val = (2 + np.random.random()) * (
          1 + np.random.random()
      )
      mean_func = lambda x: np.array([mean_func_const_val] * len(x))
      noise_var = 0.05 * np.random.random()
      Z_init = np.zeros((0, fidel_dim))
      X_init = np.zeros((0, domain_dim))
      Y_init = np.zeros((0))
      init_gp = euclidean_gp.EuclideanMFGP(
          Z_init,
          X_init,
          Y_init,
          None,
          kernel_scale,
          fidel_kernel,
          domain_kernel,
          mean_func,
          noise_var,
      )
      # Now construct the data
      post_gp = deepcopy(init_gp)
      Z_data = np.random.random((num_data, fidel_dim))
      X_data = np.random.random((num_data, domain_dim))
      Y_wo_noise = post_gp.draw_mf_samples(1, Z_data, X_data).ravel()
      Y_data = Y_wo_noise + np.random.normal(
          0, np.sqrt(noise_var), Y_wo_noise.shape
      )
      post_gp.add_mf_data_multiple(Z_data, X_data, Y_data)
      # Put everything in a namespace
      ret = Namespace(
          init_gp=init_gp,
          post_gp=post_gp,
          Z_data=Z_data,
          X_data=X_data,
          Y_data=Y_data,
          fidel_kernel=fidel_kernel,
          domain_kernel=domain_kernel,
          mean_func=mean_func,
          noise_var=noise_var,
          kernel_scale=kernel_scale,
          fidel_dim=fidel_dim,
          domain_dim=domain_dim,
          num_data=num_data,
      )
      return ret
  
imports: from __future__ import division
  from argparse import Namespace
  from copy import deepcopy
  import numpy
  from .None import kernel
  from .unittest_euclidean_gp import fit_se_gp_with_dataset, fit_matern_gp_with_dataset, fit_poly_gp_with_dataset
  from .None import euclidean_gp
  from ..utils.base_test_class import BaseTestClass, execute_tests
  from ..utils.general_utils import compute_average_sq_prediction_error
  from ..utils.option_handler import load_options
label_desc: Use image node_id 6 for calling a global function with example usage: get_init_and_post_gp(fidel_dim, domain_dim, num_data, kernel_scale, dim_bw_power) and returns: ret