code: def _generate_x_star(num_params: int, num_levels: int) -> np.ndarray:
      """Generate an 1-by-num_params array to represent initial position for EE
  
      This should be a randomly generated array in the p level grid
      :math:`\\omega`
  
      Parameters
      ---------
      num_params : int
          The number of parameters (factors)
      num_levels : int
          The number of levels
  
      Returns
      -------
      numpy.ndarray
          The initial starting positions of the trajectory
      """
      x_star = np.zeros((1, num_params))
      delta = _compute_delta(num_levels)
      bound = 1 - delta
      grid = np.linspace(0, bound, int(num_levels / 2))
  
      x_star[0, :] = rd.choice(grid, num_params)
  
      return x_star
  
imports: import numpy
  from typing import Dict
  import numpy.random
  import warnings
  from .local import LocalOptimisation
  from .brute import BruteForce
  from .strategy import SampleMorris
  from SALib.sample import common_args
  from SALib.util import scale_samples, read_param_file, compute_groups_matrix, _define_problem_with_groups, _compute_delta, _check_groups
label_desc: Use image node_id 6 for calling a global function with example usage: _generate_x_star(num_params, num_levels) and returns: x_star