class_docstr: Base class for acquisition functions.
  
  Acquisition Functions are (relatively) inexpensive functions that guide
  Bayesian Optimization search. Typically, their values at points will
  correspond to how desirable it is to evaluate the function at that point.
  This desirability can come in the form of improving information about the
  black box function (exploration) or trying to find an extrema given past
  evaluations (exploitation).
  
  TFP acquisition functions are callable objects that may be instantiated with
  subclass-specific parameters. This design enables expensive one-time
  computation to be run in `__init__` before the acquisition function is called
  repeatedly, for example during optimization. The `AcquisitionFunction` base
  class is instantiated with a predictive distribution (typically an instance of
  `tfd.GaussianProcessRegressionModel`, `tfd.StudentTProcessRegressionModel`, or
  `tfp.experimental.distributions.MultiTaskGaussianProcessRegressionModel`),
  previously-observed function values, and an optional random seed. The
  `__call__` method evaluates the acquisition function.
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docstr: Python `bool` indicating whether the acquisition function is parallel.
  
  Parallel (batched) acquisition functions evaluate batches of points rather
  than single points.
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code: def is_parallel(self):
      """Python `bool` indicating whether the acquisition function is parallel.
  
      Parallel (batched) acquisition functions evaluate batches of points rather
      than single points.
      """
      return False
  
file_path: probability/tensorflow_probability/python/experimental/bayesopt/acquisition/acquisition_function.py
imports: import abc
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 5 for calling the AcquisitionFunction obj's underlying member method code with example usage: obj.is_parallel() and returns: False