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: Automatically wrap NumPy arrays assigned to attributes.
docstr_tok: ["Automatically","wrap","NumPy","arrays","assigned","to","attributes","."]
code: def __setattr__(self, name, value):
      """Automatically wrap NumPy arrays assigned to attributes."""
      # TODO(b/126429928): Consider supporting lists/tuples.
      if isinstance(value, (np.ndarray, np.generic)):
          try:
              existing = super(NumpyState, self).__getattribute__(name)
              existing.array = value
              return
          except AttributeError:
              value = _NumpyWrapper(value)
              self._track_trackable(value, name=name, overwrite=True)
      elif name not in (
          "_self_setattr_tracking",
          "_self_update_uid",
          # TODO(b/130295584): Remove these non-_self aliases when
          # sync issues are resolved.
          "_setattr_tracking",
          "_update_uid",
      ) and getattr(self, "_setattr_tracking", True):
          # Mixing restore()-created attributes with user-added checkpointable
          # objects is tricky, since we can't use the `_lookup_dependency` trick to
          # re-create attributes (we might accidentally steal the restoration for
          # another checkpointable object). For now `NumpyState` objects must be
          # leaf nodes. Theoretically we could add some extra arguments to
          # `_lookup_dependency` to figure out whether we should create a NumPy
          # array for the attribute or not.
          raise NotImplementedError(
              (
                  "Assigned %s to the %s property of %s, which is not a NumPy"
                  " array. Currently mixing NumPy arrays and other checkpointable"
                  " objects is not supported. File a feature request if this"
                  " limitation bothers you."
              )
              % (value, name, self)
          )
      super(NumpyState, self).__setattr__(name, 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 3 for calling the NumpyState obj's underlying member method code with example usage: obj.__setattr__(name, value) without return types