code: def synthetic_log_gaussian_cox_process(
      shuffle=True,
      shuffle_seed=1337,
  ):
      """Synthetic dataset sampled from the LogGaussianCoxProcess model.
  
      This dataset was simulated by constructing a 10 by 10 grid of equidistant 2D
      locations with spacing = 1, and then sampling from the prior to determine the
      counts at those locations.
  
      The data are encoded into three parallel arrays. I.e.
      `train_counts[i]` and `train_extents[i]` correspond to `train_locations[i]`.
  
      Args:
        shuffle: Whether to shuffle the dataset.
        shuffle_seed: Seed to use when shuffling.
  
      Returns:
        dataset: A Dict with the following keys:
          train_locations: Float `Tensor` with shape `[num_train_points, 2]`.
            Training set locations where counts were measured.
          train_extents: Float `Tensor` with shape `[num_train_points]`. Training
            set location extents, must be positive.
          train_counts: Float `Tensor` with shape `[num_train_points]`. Training set
            counts, must be positive.
      """
      locations = synthetic_log_gaussian_cox_process_lib.LOCATIONS
      extents = synthetic_log_gaussian_cox_process_lib.EXTENTS
      counts = synthetic_log_gaussian_cox_process_lib.COUNTS
  
      if shuffle:
          shuffle_idxs = np.arange(locations.shape[0])
          np.random.RandomState(shuffle_seed).shuffle(shuffle_idxs)
          locations = locations[shuffle_idxs]
          counts = counts[shuffle_idxs]
          extents = extents[shuffle_idxs]
  
      return dict(
          train_locations=locations,
          train_extents=extents,
          train_counts=counts,
      )
  
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 13 for calling a global function with example usage: synthetic_log_gaussian_cox_process(shuffle, shuffle_seed) and returns: dict