docstr: Get the new suggested trials with random search.
code: def get_new_suggestions(self, study_name, trials=[], number=1):
      """
      Get the new suggested trials with random search.
      """
  
      return_trial_list = []
  
      study = Study.objects.get(name=study_name)
      study_configuration_json = json.loads(study.study_configuration)
      params = study_configuration_json["params"]
  
      for i in range(number):
          trial = Trial.create(study.name, "RandomSearchTrial")
          parameter_values_json = {}
  
          for param in params:
              if param["type"] == "DOUBLE":
                  suggest_value = AlgorithmUtil.get_random_value(
                      param["minValue"], param["maxValue"]
                  )
  
              elif param["type"] == "INTEGER":
                  suggest_value = AlgorithmUtil.get_random_int_value(
                      param["minValue"], param["maxValue"]
                  )
  
              elif param["type"] == "DISCRETE":
                  feasible_point_list = [
                      float(value.strip())
                      for value in param["feasiblePoints"].split(",")
                  ]
                  suggest_value = (
                      AlgorithmUtil.get_random_item_from_list(
                          feasible_point_list
                      )
                  )
  
              elif param["type"] == "CATEGORICAL":
                  feasible_point_list = [
                      value.strip()
                      for value in param["feasiblePoints"].split(",")
                  ]
                  suggest_value = (
                      AlgorithmUtil.get_random_item_from_list(
                          feasible_point_list
                      )
                  )
  
              parameter_values_json[
                  param["parameterName"]
              ] = suggest_value
  
          trial.parameter_values = json.dumps(parameter_values_json)
          trial.save()
          return_trial_list.append(trial)
  
      return return_trial_list
  
file_path: advisor/advisor_server/suggestion/algorithm/random_search.py
imports: import json
  import random
  from suggestion.models import Study
  from suggestion.models import Trial
  from suggestion.algorithm.abstract_algorithm import AbstractSuggestionAlgorithm
  from suggestion.algorithm.util import AlgorithmUtil
label_desc: Use image node_id 1 for calling the RandomSearchAlgorithm obj's underlying member method code with example usage: obj.get_new_suggestions(study_name, trials, number) and returns: return_trial_list