code: def test_min_nobs(basic_data):
      y, x, w = basic_data
      if not np.any(np.isnan(np.asarray(x))):
          return
      mod = RollingOLS(y, x, 150)
      res = mod.fit()
      # Ensures that the constraint binds
      min_nobs = res.nobs[res.nobs != 0].min() + 1
      mod = RollingOLS(y, x, 150, min_nobs=min_nobs)
      res = mod.fit()
      assert np.all(res.nobs[res.nobs != 0] >= min_nobs)
  
imports: from io import BytesIO
  from itertools import product
  import warnings
  import numpy
  import pandas
  import pytest
  from numpy.testing import assert_allclose, assert_array_equal
  from statsmodels import tools
  from statsmodels.regression.linear_model import WLS
  from statsmodels.regression.rolling import RollingWLS, RollingOLS
label_desc: Use image node_id 17 for calling a global function with example usage: test_min_nobs(basic_data) without return types