code: def test_TEVA():
      size = 50
      df = pl.DataFrame(
          {
              "open": np.random.uniform(
                  low=0.0, high=100.0, size=size
              ).astype("float32"),
              "high": np.random.uniform(
                  low=0.0, high=100.0, size=size
              ).astype("float32"),
              "low": np.random.uniform(
                  low=0.0, high=100.0, size=size
              ).astype("float32"),
              "close": np.random.uniform(
                  low=0.0, high=100.0, size=size
              ).astype("float32"),
              "volume": np.random.uniform(
                  low=0.0, high=100.0, size=size
              ).astype("float32"),
          }
      )
      tema1 = abstract.TEMA(df, timeperiod=9)
      assert isinstance(tema1, pl.Series)
      assert len(tema1) == 50
      inputs = abstract.TEMA.get_input_arrays()
      assert inputs.columns == df.columns
      for column in df.columns:
          assert_array_equal(
              inputs[column].to_numpy(), df[column].to_numpy()
          )
  
      tema2 = abstract.TEMA(df, timeperiod=9)
      assert isinstance(tema2, pl.Series)
      assert len(tema2) == 50
      inputs = abstract.TEMA.get_input_arrays()
      assert inputs.columns == df.columns
      for column in df.columns:
          assert_array_equal(
              inputs[column].to_numpy(), df[column].to_numpy()
          )
  
      assert_array_equal(tema1.to_numpy(), tema2.to_numpy())
  
file_path: ta-lib-python/tests/test_polars.py
imports: import numpy
  from numpy.testing import assert_array_equal
  import polars
  import talib
  from talib import abstract
label_desc: Use image node_id 3 for calling a global function with example usage: test_TEVA() without return types