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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _preprocess_individuals(self, individuals):
 """Preprocess DEAP individuals before pipeline evaluation. Parameters individuals: a list of DEAP individual One individual is a list of pipeline operators and model parameters that can be compiled by DEAP into a callable function Returns ------- operator_counts: dictionary a dictionary of operator counts in individuals for evaluation eval_individuals_str: list a list of string of individuals for evaluation sklearn_pipeline_list: list a list of scikit-learn pipelines converted from DEAP individuals for evaluation stats_dicts: dictionary A dict where 'key' is the string representation of an individual and 'value' is a dict containing statistics about the individual """

        # update self._pbar.total
        if not (self.max_time_mins is None) and not self._pbar.disable and self._pbar.total <= self._pbar.n:
            self._pbar.total += self._lambda
        # Check we do not evaluate twice the same individual in one pass.
        _, unique_individual_indices = np.unique([str(ind) for ind in individuals], return_index=True)
        unique_individuals = [ind for i, ind in enumerate(individuals) if i in unique_individual_indices]
        # update number of duplicate pipelines
        self._update_pbar(pbar_num=len(individuals) - len(unique_individuals))

        # a dictionary for storing operator counts
        operator_counts = {}
        stats_dicts = {}
        # 2 lists of DEAP individuals' string, their sklearn pipelines for parallel computing
        eval_individuals_str = []
        sklearn_pipeline_list = []

        for individual in unique_individuals:
            # Disallow certain combinations of operators because they will take too long or take up too much RAM
            # This is a fairly hacky way to prevent TPOT from getting stuck on bad pipelines and should be improved in a future release
            individual_str = str(individual)
            if not len(individual): # a pipeline cannot be randomly generated
                self.evaluated_individuals_[individual_str] = self._combine_individual_stats(5000.,
                                                                                             -float('inf'),
                                                                                             individual.statistics)
                self._update_pbar(pbar_msg='Invalid pipeline encountered. Skipping its evaluation.')
                continue
            sklearn_pipeline_str = generate_pipeline_code(expr_to_tree(individual, self._pset), self.operators)
            if sklearn_pipeline_str.count('PolynomialFeatures') > 1:
                self.evaluated_individuals_[individual_str] = self._combine_individual_stats(5000.,
                                                                                             -float('inf'),
                                                                                             individual.statistics)
                self._update_pbar(pbar_msg='Invalid pipeline encountered. Skipping its evaluation.')
            # Check if the individual was evaluated before
            elif individual_str in self.evaluated_individuals_:
                self._update_pbar(pbar_msg=('Pipeline encountered that has previously been evaluated during the '
                                            'optimization process. Using the score from the previous evaluation.'))
            else:
                try:
                    # Transform the tree expression into an sklearn pipeline
                    sklearn_pipeline = self._toolbox.compile(expr=individual)

                    # Fix random state when the operator allows
                    self._set_param_recursive(sklearn_pipeline.steps, 'random_state', 42)
                    # Setting the seed is needed for XGBoost support because XGBoost currently stores
                    # both a seed and random_state, and they're not synced correctly.
                    # XGBoost will raise an exception if random_state != seed.
                    if 'XGB' in sklearn_pipeline_str:
                        self._set_param_recursive(sklearn_pipeline.steps, 'seed', 42)

                    # Count the number of pipeline operators as a measure of pipeline complexity
                    operator_count = self._operator_count(individual)
                    operator_counts[individual_str] = max(1, operator_count)

                    stats_dicts[individual_str] = individual.statistics
                except Exception:
                    self.evaluated_individuals_[individual_str] = self._combine_individual_stats(5000.,
                                                                                                 -float('inf'),
                                                                                                 individual.statistics)
                    self._update_pbar()
                    continue
                eval_individuals_str.append(individual_str)
                sklearn_pipeline_list.append(sklearn_pipeline)

        return operator_counts, eval_individuals_str, sklearn_pipeline_list, stats_dicts