code: def arma_model():
      # Autoregressive Moving Average (ARMA)
      np.random.seed(12345)
      arparams = np.array([1, -0.75, 0.25])
      maparams = np.array([1, 0.65, 0.35])
      nobs = 250
      y = arma_generate_sample(arparams, maparams, nobs)
      dates = pd.date_range("1980-1-1", freq="M", periods=nobs)
      y = pd.Series(y, index=dates)
  
      arima = ARIMA(y, order=(2, 0, 2), trend="n")
      model = arima.fit()
      inference_dataframe = pd.DataFrame(
          [["1999-06-30", "2001-05-31"]], columns=["start", "end"]
      )
  
      return ModelWithResults(
          model=model,
          alg=arima,
          inference_dataframe=inference_dataframe,
      )
  
imports: from collections import namedtuple
  import numpy
  import pandas
  import statsmodels.api
  from scipy.linalg import toeplitz
  from statsmodels.tsa.arima.model import ARIMA
  from statsmodels.tsa.arima_process import arma_generate_sample
  from mlflow.models import ModelSignature
  from mlflow.types.schema import Schema, TensorSpec
label_desc: Use image node_id 14 for calling a global function with example usage: arma_model() and returns: ModelWithResults