code: def sp500_log_returns(num_points=None):
      """Dataset of mean-adjusted log returns of the S&P 500 index.
  
      A log return is the change in the log-transformed closing prices, which also
      corresponds to the log of relative returns (current price divided by previous
      price).
  
      Each of the 2516 entries represents the adjusted log return of the daily
      closing price relative to the previous close, for each (non-holiday) weekday,
      beginning 6/26/2010 and ending 6/24/2020.
  
      Args:
        num_points: Optional `int` length of the series to return. If specified,
          only the final `num_points` returns are centered and returned.
          Default value: `None`.
      Returns:
        dataset: A Dict with the following keys:
          `centered_returns`: float `Tensor` daily returns, minus the mean return.
      """
      returns = np.diff(np.log(sp500_closing_prices_lib.CLOSING_PRICES))
      num_points = num_points or len(returns)
      return dict(
          centered_returns=returns[-num_points:]
          - np.mean(returns[-num_points:])
      )
  
imports: import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from inference_gym.internal.datasets import brownian_motion_missing_middle_observations
  from inference_gym.internal.datasets import convection_lorenz_bridge
  from inference_gym.internal.datasets import sp500_closing_prices
  from inference_gym.internal.datasets import synthetic_item_response_theory
  from inference_gym.internal.datasets import synthetic_log_gaussian_cox_process
  from inference_gym.internal.datasets import synthetic_plasma_spectroscopy
  from inference_gym.internal.datasets import synthetic_plasma_spectroscopy_with_bump
label_desc: Use image node_id 11 for calling a global function with example usage: sp500_log_returns(num_points) and returns: dict