class_docstr: Stochastic volatility model of 100 days of S&P500 log returns.
class_docstr_tok: ["Stochastic","volatility","model","of","100","days","of","S","&","P500","log","returns","."]
code: def __init__(
      self,
      centered=False,
      use_fft=True,
  ):
      dataset = data.sp500_log_returns(num_points=100)
      super(VectorizedStochasticVolatilityLogSP500Small, self).__init__(
          name="vectorized_stochasticic_volatility_log_sp500_small",
          pretty_name=(
              "Smaller stochastic volatility model of S&P500 log returns."
          ),
          **dataset,
      )
  
imports: import collections
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import prefer_static
  from inference_gym.internal import data
  from inference_gym.targets import bayesian_model
  from inference_gym.targets import model
  from inference_gym.targets.ground_truth import stochastic_volatility_log_sp500
  from inference_gym.targets.ground_truth import stochastic_volatility_log_sp500_small
  from inference_gym.targets.ground_truth import stochastic_volatility_sp500
  from inference_gym.targets.ground_truth import stochastic_volatility_sp500_small
label_desc: Use image node_id 1 to create a new VectorizedStochasticVolatilityLogSP500Small object from inherited base classes: VectorizedStochasticVolatility with example: obj = VectorizedStochasticVolatilityLogSP500Small(centered, use_fft)