class_docstr: Suggests an architecture based on the Harmonica algorithm.
docstr: Randomly samples architectures, predict loss, and return minimal.
  
  Args:
    feature_extender: sklearn PolynomialFeatures extnder.
    num_samples: the number of samples from the search space the function will
      try before returning the minimal point. If the search space over the
      relevant variables is smaller than num_samples, then, the method will
      also fix non-relevant variable to zero and search the whole space of the
      relevant variables.
    coefficients: coefficients of the polynomial model that predicts the loss.
    relevant_variables: set of indices of the most relevant variables in the
      linear model.
  
  Returns:
    A Phoenix architecture (np.array of ints) with minimal predicted loss.
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code: def _get_good_architecture(
      self,
      feature_extender,
      num_samples,
      coefficients,
      relevant_variables=None,
  ):
      """Randomly samples architectures, predict loss, and return minimal.
  
      Args:
        feature_extender: sklearn PolynomialFeatures extnder.
        num_samples: the number of samples from the search space the function will
          try before returning the minimal point. If the search space over the
          relevant variables is smaller than num_samples, then, the method will
          also fix non-relevant variable to zero and search the whole space of the
          relevant variables.
        coefficients: coefficients of the polynomial model that predicts the loss.
        relevant_variables: set of indices of the most relevant variables in the
          linear model.
  
      Returns:
        A Phoenix architecture (np.array of ints) with minimal predicted loss.
      """
      if self._seed:
          np.random.seed(seed=self._seed)
  
      assignments = []
      architectures = []
      relevant_blocks = None
      if relevant_variables:
          logging.info("We have relevant variables")
          # From relevant variable in the fourier space to relevant categorical
          # Variables in the original space.
          relevant_blocks = set(
              [
                  i // len(self._block_indices)
                  for i in relevant_variables
              ]
          )
  
      # If true, we can search the whole space (the number of samples allowed
      # is less than the whole space).
      if (
          relevant_blocks
          and len(self._block_indices) ** len(relevant_blocks)
          < num_samples
      ):
          # All possible assignments - Creating the search space with cartesian
          # product
          search_space = [[0]] * self._num_params
          for i in relevant_blocks:
              search_space[i] = list(range(len(self._block_indices)))
          all_arch = np.array(np.meshgrid(*search_space)).T.reshape(
              -1, self._num_params
          )
          for data_point in all_arch:
              arch_list = [self._block_indices[i] for i in data_point]
              arch_list = architecture_utils.fix_architecture_order(
                  arch_list, self._phoenix_spec.problem_type
              )
              arc = np.array(arch_list)
              architectures.append(arc)
              # Translates to two assignments to predict loss.
              assignments += (
                  self.translate_architecture_to_feature_assignment(arc)
              )
  
      for i in range(num_samples):
          rand_arch = np.random.randint(
              len(self._block_indices),
              size=self._phoenix_spec.minimum_depth,
          )
          arch_list = [self._block_indices[i] for i in rand_arch]
          # Translate to a valid architecture for Phoenix.
          arch_list = architecture_utils.fix_architecture_order(
              arch_list, self._phoenix_spec.problem_type
          )
          arch = np.array(arch_list)
          architectures.append(arch)
          # Translate to two assignments to predict loss.
          assignments += (
              self.translate_architecture_to_feature_assignment(arch)
          )
  
      # Expand to polynomial features.
      assignments = self._get_polynomial_expansion(
          feature_extender, assignments
      )
      # Predict loss based on coefficient of the linear model
      predictions_real_and_imag = np.square(
          np.matmul(assignments, coefficients)
      ).reshape(-1, 2)
  
      # Summing the square of the real and imaginary values.
      predictions_sum_square_value = predictions_real_and_imag.sum(
          axis=1
      )
      # Index of the minimal loos
      minimal_index = np.argmin(predictions_sum_square_value)
      # Return the Phoenix architecutre for the minimal loss
      return architectures[minimal_index]
  
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 8 for calling the Harmonica obj's underlying member method code with example usage: obj._get_good_architecture(feature_extender, num_samples, coefficients, relevant_variables) and returns: architectures