class_docstr: A checkpointable object whose NumPy array attributes are saved/restored.
  
  Example usage:
  
  ```python
  arrays = numpy_storage.NumpyState()
  checkpoint = tf.train.Checkpoint(numpy_arrays=arrays)
  arrays.x = np.ones([3, 4])
  directory = self.get_temp_dir()
  prefix = os.path.join(directory, 'ckpt')
  save_path = checkpoint.save(prefix)
  arrays.x[:] = 0.
  assert (arrays.x == np.zeros([3, 4])).all()
  checkpoint.restore(save_path)
  assert (arrays.x == np.ones([3, 4])).all()
  
  second_checkpoint = tf.train.Checkpoint(
      numpy_arrays=numpy_storage.NumpyState())
  # Attributes of NumpyState objects are created automatically by restore()
  second_checkpoint.restore(save_path)
  assert (second_checkpoint.numpy_arrays.x == np.ones([3, 4])).all()
  ```
  
  Note that `NumpyState` objects re-create the attributes of the previously
  saved object on `restore()`. This is in contrast to TensorFlow variables, for
  which a `Variable` object must be created and assigned to an attribute.
  
  This snippet works both when graph building and when executing eagerly. On
  save, the NumPy array(s) are fed as strings to be saved in the checkpoint (via
  a placeholder when graph building, or as a string constant when executing
  eagerly). When restoring they skip the TensorFlow graph entirely, and so no
  restore ops need be run. This means that restoration always happens eagerly,
  rather than waiting for `checkpoint.restore(...).run_restore_ops()` like
  TensorFlow variables when graph building.
docstr: Un-wrap `_NumpyWrapper` objects when accessing attributes.
docstr_tok: ["Un-wrap","`","_NumpyWrapper","`","objects","when","accessing","attributes","."]
code: def __getattribute__(self, name):
      """Un-wrap `_NumpyWrapper` objects when accessing attributes."""
      value = super(NumpyState, self).__getattribute__(name)
      if isinstance(value, _NumpyWrapper):
          return value.array
      return value
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import io
  import numpy
  import tensorflow
  from tf_agents.specs import array_spec
  from tensorflow.python.trackable import base
  from tensorflow.python.trackable import data_structures
label_desc: Use image node_id 2 for calling the NumpyState obj's underlying member method code with example usage: obj.__getattribute__(name) and returns: value, value