class_docstr: Suggests an architecture based on the Harmonica algorithm.
docstr: Returns all previous trials results as assignments and loss.
docstr_tok: ["Returns","all","previous","trials","results","as","assignments","and","loss","."]
code: def batch_sample(self, trials):
      """Returns all previous trials results as assignments and loss."""
      completed = trials
      x = []
      y = []
      for trial in completed:
          arc = architecture_utils.get_architecture(
              architecture_utils.DirectoryHandler.trial_dir(trial)
          )
          # Returns two assignments to fit. One for the real values and one for
          # The imaginary
          x += self.translate_architecture_to_feature_assignment(arc)
          # Two objectives, one is for the real values assignment. The other is
          # zero for the imaginary assignment.
          y += [trial.final_measurement.objective_value, 0]
      return x, y
  
imports: import cmath
  import os
  from absl import logging
  from model_search import block_builder
  from model_search.architecture import architecture_utils
  from model_search.search import search_algorithm
  import numpy
  from sklearn import linear_model
  from sklearn import preprocessing
  import tensorflow.compat.v2
label_desc: Use image node_id 5 for calling the Harmonica obj's underlying member method code with example usage: obj.batch_sample(trials) and returns: x,  y