class_docstr: Explores a grid across the hyperparameter space such that every pairing is
  evaluated.
  
  For continuous and discrete parameters grid points are picked within the
  range. For example, a continuous parameter with range [1, 2] with two grid
  points would have points 1 1/3 and 1 2/3. For three points, 1 1/4, 1 1/2,
  and 1 3/4.
  
  Example:
  ::
  
      hp_space = {'act': ['tanh', 'relu'],
                  'lrinit': [0.1, 0.01],
                  }
      parameters = sherpa.Parameter.grid(hp_space)
      alg = sherpa.algorithms.GridSearch()
  
  Args:
      num_grid_points (int): number of grid points for continuous / discrete.
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code: def __init__(self, num_grid_points=2):
      self.grid = None
      self.num_grid_points = num_grid_points
      self.count = 0
  
imports: import os
  import random
  import numpy
  import logging
  import sherpa
  import pandas
  import sklearn.gaussian_process
  from sherpa.core import Choice, Continuous, Discrete, Ordinal, AlgorithmState
  from sherpa.core import rng
  import sklearn.model_selection
  import collections
label_desc: Use image node_id 1 to create a new GridSearch object from inherited base classes: Algorithm with example: obj = GridSearch(num_grid_points)