code: def test_arima_inverse_box_cox_correct():
      """Tests if negative values after box-cox transformation are correct (not nan) after inverse box-cox"""
      ts = np.random.uniform(0, 100, 1000)
      input_ts_len = len(ts)
      arima = ARIMAImplementation()
  
      _, arima.lambda_value = stats.boxcox(ts)
  
      nan_inds = np.random.randint(1, 999, size=10)
      nan_inds = np.append(nan_inds, [0, int(len(ts) - 1)])
      ts[nan_inds] = -10
      ts = arima._inverse_boxcox(ts, arima.lambda_value)
  
      assert len(np.ravel(np.argwhere(np.isnan(ts)))) == 0
      assert input_ts_len == len(ts)
  
imports: from copy import deepcopy
  from typing import Optional
  import numpy
  import pandas
  import pytest
  from scipy import stats
  from sklearn.metrics import mean_absolute_error, mean_squared_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.arima import ARIMAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import AutoRegImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.ts_wrappers import in_sample_ts_forecast, out_of_sample_ts_forecast
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
  from fedot.utilities.synth_dataset_generator import generate_synthetic_data
label_desc: Use image node_id 9 for calling a global function with example usage: test_arima_inverse_box_cox_correct() without return types