code: def test_basic_arma():
      arma = ARIMA(order=(0, 0, 0), suppress_warnings=True)
      preds = arma.fit_predict(y)  # fit/predict for coverage
  
      # No OOB, so assert none
      assert arma.oob_preds_ is None
  
      # test some of the attrs
      assert_almost_equal(
          arma.aic(), 11.201, decimal=3
      )  # equivalent in R
  
      # intercept is param 0
      intercept = arma.params()[0]
      assert_almost_equal(
          intercept, 0.441, decimal=3
      )  # equivalent in R
      assert_almost_equal(arma.aicc(), 11.74676, decimal=5)
      assert_almost_equal(arma.bic(), 13.639060053303311, decimal=5)
  
      # get predictions
      expected_preds = np.array(
          [
              0.44079876,
              0.44079876,
              0.44079876,
              0.44079876,
              0.44079876,
              0.44079876,
              0.44079876,
              0.44079876,
              0.44079876,
              0.44079876,
          ]
      )
  
      # generate predictions
      assert_array_almost_equal(preds, expected_preds)
  
      # Make sure we can get confidence intervals
      expected_intervals = np.array(
          [
              [-0.10692387, 0.98852139],
              [-0.10692387, 0.98852139],
              [-0.10692387, 0.98852139],
              [-0.10692387, 0.98852139],
              [-0.10692387, 0.98852139],
              [-0.10692387, 0.98852139],
              [-0.10692387, 0.98852139],
              [-0.10692387, 0.98852139],
              [-0.10692387, 0.98852139],
              [-0.10692387, 0.98852139],
          ]
      )
  
      _, intervals = arma.predict(
          n_periods=10, return_conf_int=True, alpha=0.05
      )
      assert_array_almost_equal(intervals, expected_intervals)
  
imports: import numpy
  import pandas
  from pmdarima.arima import ARIMA, auto_arima, AutoARIMA, ARMAtoMA
  from pmdarima.arima import _validation
  from pmdarima.compat.pytest import pytest_error_str
  from pmdarima.datasets import load_lynx, load_wineind, load_heartrate
  from numpy.random import RandomState
  from numpy.testing import assert_array_almost_equal, assert_almost_equal, assert_allclose
  from statsmodels import api
  from sklearn.metrics import mean_squared_error
  import datetime
  import joblib
  import os
  import pickle
  import pytest
  import tempfile
  import time
label_desc: Use image node_id 2 for calling a global function with example usage: test_basic_arma() without return types