code: def summarize(values, epsilon):
      """Reduce a 1D sequence of values to a summary.
  
      This algorithm is based on numpy.quantiles but modified to allow for
      intermediate steps between multiple data sets. It first finds the target
      number of bins as the reciprocal of epsilon and then takes the individual
      values spaced at appropriate intervals to arrive at that target.
      The final step is to return the corresponding counts between those values
      If the target num_bins is larger than the size of values, the whole array is
      returned (with weights of 1).
  
      Args:
          values: 1D `np.ndarray` to be summarized.
          epsilon: A `'float32'` that determines the approximate desired
          precision.
  
      Returns:
          A 2D `np.ndarray` that is a summary of the inputs. First column is the
          interpolated partition values, the second is the weights (counts).
      """
      values = np.reshape(values, [-1])
      values = np.sort(values)
      elements = np.size(values)
      num_buckets = 1.0 / epsilon
      increment = elements / num_buckets
      start = increment
      step = max(increment, 1)
      boundaries = values[int(start) :: int(step)]
      weights = np.ones_like(boundaries)
      weights = weights * step
      return np.stack([boundaries, weights])
  
imports: import numpy
  from keras import backend
  from keras.api_export import keras_export
  from keras.layers.preprocessing.tf_data_layer import TFDataLayer
  from keras.utils import argument_validation
  from keras.utils import numerical_utils
  from keras.utils.module_utils import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: summarize(values, epsilon) and returns: np