docstr: Checks that you get finite values given unconstrained samples.
  
  We check `log_prob` as well as the values of the expectations.
  
  Args:
    centered: Whether or not to use the centered parameterization.
    use_fft: Whether or not to use FFT-based convolution to implement the
      centering transformation.
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code: def testBasic(self, centered, use_fft):
      """Checks that you get finite values given unconstrained samples.
  
      We check `log_prob` as well as the values of the expectations.
  
      Args:
        centered: Whether or not to use the centered parameterization.
        use_fft: Whether or not to use FFT-based convolution to implement the
          centering transformation.
      """
      model = vectorized_stochastic_volatility.VectorizedStochasticVolatility(
          centered_returns=_test_dataset(),
          centered=centered,
          use_fft=use_fft,
      )
      self.validate_log_prob_and_transforms(
          model,
          sample_transformation_shapes=dict(
              identity={
                  "persistence_of_volatility": [],
                  "mean_log_volatility": [],
                  "white_noise_shock_scale": [],
                  "log_volatility": [5],
              }
          ),
      )
  
imports: import functools
  from absl import logging
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.python.internal import test_util
  from inference_gym.internal import test_util
  from inference_gym.targets import vectorized_stochastic_volatility
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 for calling the VectorizedStochasticVolatilityTest obj's underlying member method code with example usage: obj.testBasic(centered, use_fft) without return types