code: def is_grid_search_deployed(self, environment=None):
      if environment is None:
          environment = (
              h2o_cloud_extensions.settings.mlops.deployment_environment
          )
      elif isinstance(environment, str):
          environment = [
              environment,
          ]
      assert isinstance(environment, list)
      if self.models is None or len(self.models) == 0:
          warnings.warn(
              "The grid search instance doesn't contain any trained model."
          )
          return True
      else:
          mlops_connection = create_mlops_connection()
          project = get_or_create_project(mlops_connection)
          return all(
              [
                  _is_deployed(
                      model, mlops_connection, project, environment
                  )
                  for model in self.models
              ]
          )
  
imports: import h2o_cloud_extensions
  import h2o_mlops_client
  import time
  import h2o
  from .status import _is_model_deployed, _is_model_published
  from .connect import *
  from .utils import *
label_desc: Use image node_id 11 for calling a global function with example usage: is_grid_search_deployed(environment) and returns: True, all