code: def failing_logit_model():
      X = pd.DataFrame(
          {
              "x0": np.array(
                  [2.0, 3.0, 1.0, 2.0, 20.0, 30.0, 10.0, 20.0]
              ),
              "x1": np.array(
                  [2.0, 3.0, 1.0, 2.0, 20.0, 30.0, 10.0, 20.0]
              ),
          },
          columns=["x0", "x1"],
      )
      y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
      # building the model and fitting the data
      log_reg = sm.Logit(y, X)
      model = log_reg.fit()
  
      return ModelWithResults(
          model=model, alg=log_reg, inference_dataframe=X
      )
  
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 3 for calling a global function with example usage: failing_logit_model() and returns: ModelWithResults