code: def radon_indiana(
      train_fraction=1.0,
      shuffle=False,
      shuffle_seed=42,
  ):
      """The Radon dataset for the state of Indiana.
  
      See `radon` for more details. For this dataset, `num_examples = 1914` and
      `num_counties = 91`. This dataset had 63 datapoints with floors above 1, which
      are filtered out.
  
      Args:
        train_fraction: What fraction of the data to put in the training set.
        shuffle: `bool`. If `True`, shuffle the data.
        shuffle_seed: `int`, RNG seed to use if shuffling the data.
  
      Returns:
        dataset: A Dict with the following keys:
          `num_counties`: `int`, number of unique counties in the filtered dataset.
          `train_log_uranium`: Floating-point `Tensor` with shape
            `[num_train]`. Soil uranium measurements.
          `train_floor`: Integer `Tensor` with shape `[num_train]`. Floor of the
            house on which the measurement was taken.
          `train_county`: Integer `Tensor` with values in `range(0, num_counties)`
            of shape `[num_train]`. County in which the measurement was taken.
          `train_floor_by_county`: Floating-point `Tensor` with shape
            `[num_train]`. Average floor on which the measurement was taken for the
            county in which each house is located (the `Tensor` will have
            `num_counties` unique values). This represents the contextual effect.
          `train_log_radon`: Floating-point `Tensor` with shape `[num_train]`.
            Radon measurement for each house (the dependent variable in the model).
          `test_log_uranium`: Floating-point `Tensor` with shape `[num_test`. Soil
            Soil uranium measurements for the test set.
          `test_floor`: Integer `Tensor` with shape `[num_test]`. Floor of the house
            house on which the measurement was taken.
          `test_county`: Integer `Tensor` with values in `range(0, num_counties)` of
            shape `[num_test]`. County in which the measurement was taken. Can be
            `None`, in which case test-related sample transformations are not
            computed.
          `test_floor_by_county`: Floating-point `Tensor` with shape
            `[num_test]`. Average floor on which the measurement was taken
            (calculated from the training set) for the county in which each house is
            located (the `Tensor` will have `num_counties` unique values). This
            represents the contextual effect.
          `test_log_radon`: Floating-point `Tensor` with shape `[num_test]`. Radon
            measurement for each house (the dependent variable in the model).
      """
      return radon(
          state="IN",
          num_examples=1851,
          num_counties=91,
          shuffle=shuffle,
          train_fraction=train_fraction,
          shuffle_seed=shuffle_seed,
      )
  
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 8 for calling a global function with example usage: radon_indiana(train_fraction, shuffle, shuffle_seed) and returns: radon