Synthesis_incorporation/value_search/value_search.py [988:1032]:
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        for values in value_trial_list:
            example = {"inputs": values, "output": output_value}
            predicted_operations = prediction_model.get_predicted_operations(example=example, settings=settings)
            for predicted_operation in predicted_operations:
                if len(values) > 1 and predicted_operation.name in ['torch.cat(tensors, dim)', 'torch.stack(tensors)', 'torch.stack(tensors, dim)']:
                    stacked_value = all_operations.find_operation_with_name('PairCreationOperation').apply(values, settings)
                    if stacked_value is None:
                        predicted_values = []
                    else:
                        predicted_values = predicted_operation.enumerate_values_with_values(
                            given_values=[[stacked_value]],
                            potential_value_list=constants_values,
                            end_time=end_time,
                            settings=settings,
                            statistics=statistics
                        )
                else:
                    predicted_values = predicted_operation.enumerate_values_with_values(
                        given_values=[[value] for value in values],
                        potential_value_list=constants_values,
                        end_time=end_time,
                        settings=settings,
                        statistics=statistics
                    )
                for predicted_value in predicted_values:
                    if predicted_value not in value_set:
                        if settings.printing.verbose:
                            expression = predicted_value.reconstruct_expression()
                            print("[prediction] {} produces:\n{}".format(expression, predicted_value))

                        if predicted_value == output_value:
                            end_time = _check_solution_found(predicted_value, output_value, benchmark,
                                                        0, start_time, end_time,
                                                        solutions, solution_expression_set, settings, True)
                            if len(solutions) >= settings.max_solutions:
                                return (
                                    solutions,
                                    value_set,
                                    values_by_weight,
                                    statistics,
                                )

                        else:
                            if settings.model.do_first_in_seq:
                                value_set.add(predicted_value)
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Synthesis_incorporation/value_search/value_search.py [1082:1126]:
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                            for values in value_trial_list:
                                example = {"inputs": values, "output": output_value}
                                predicted_operations = prediction_model.get_predicted_operations(example=example, settings=settings)
                                for predicted_operation in predicted_operations:
                                    if len(values) > 1 and predicted_operation.name in ['torch.cat(tensors, dim)', 'torch.stack(tensors)', 'torch.stack(tensors, dim)']:
                                        stacked_value = all_operations.find_operation_with_name('PairCreationOperation').apply(values, settings)
                                        if stacked_value is None:
                                            predicted_values = []
                                        else:
                                            predicted_values = predicted_operation.enumerate_values_with_values(
                                                given_values=[[stacked_value]],
                                                potential_value_list=constants_values,
                                                end_time=end_time,
                                                settings=settings,
                                                statistics=statistics
                                            )
                                    else:
                                        predicted_values = predicted_operation.enumerate_values_with_values(
                                            given_values=[[value] for value in values],
                                            potential_value_list=constants_values,
                                            end_time=end_time,
                                            settings=settings,
                                            statistics=statistics
                                        )
                                    for predicted_value in predicted_values:
                                        if predicted_value not in value_set:
                                            if settings.printing.verbose:
                                                expression = predicted_value.reconstruct_expression()
                                                print("[prediction] {} produces:\n{}".format(expression, predicted_value))

                                            if predicted_value == output_value:
                                                end_time = _check_solution_found(predicted_value, output_value, benchmark,
                                                                                0, start_time, end_time,
                                                                                solutions, solution_expression_set, settings, True)

                                                if len(solutions) >= settings.max_solutions:
                                                    return (
                                                        solutions,
                                                        value_set,
                                                        values_by_weight,
                                                        statistics,
                                                    )
                                            else:
                                                if settings.model.do_first_in_seq:
                                                    value_set.add(predicted_value)
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