code: def make_grid(point, space, model, n_points):
      """Build a grid based on point.
  
      The shape of the grid will be
          (number of hyperparameters,
           number of points ``n_points``,
           number of hyperparameters + 1)
  
      Last column is the objective predicted by the model for a given point.
  
      Parameters
      ----------
      point: numpy.ndarray
          A tuple representation of the best trials, (hyperparameters + objective)
      space: Space object
          A space object from an experiment. It must be flattened and linearized.
      model: `sklearn.base.RegressorMixin`
          Trained regressor used to compute predictions on the grid
      n_points: int
          Number of points for each dimension on the grid.
  
      """
      grid = numpy.zeros((len(space), n_points, len(space) + 1))
      for i, dim in enumerate(space.values()):
          grid[i, :, :] = point
          grid[i, :, i] = numpy.linspace(*dim.interval(), num=n_points)
          grid[i, :, -1] = model.predict(grid[i, :, :-1])
      return grid
  
file_path: orion/src/orion/analysis/lpi_utils.py
imports: import numpy
  import pandas
  from orion.analysis.base import flatten_numpy, to_numpy, train_regressor
  from orion.core.worker.transformer import build_required_space
label_desc: Use image node_id 1 for calling a global function with example usage: make_grid(point, space, model, n_points) and returns: grid