BVhPdZddlZddlmZejGddeZy)z*Protocol class for custom tf.nest support.N)Protocolc&eZdZdZdZedZy)CustomNestProtocola Protocol for adding custom tf.nest support in user-defined classes. User classes should implement the two methods defined in this protocol in order to be supported by nest functions. - `__tf_flatten__` for generating the flattened components and the metadata of the current object. - `__tf_unflatten__` for creating a new object based on the input metadata and the components. See the method doc for details. In terms of support level, classes implementing this protocol - are supported by tf.nest and tf.data functions. - have limited support from tf.function, which requires writing a custom TraceType subclass to be used as the input or output of a tf.function. - are NOT supported by SavedModel. Code Examples: >>> import dataclasses >>> @dataclasses.dataclass ... class MaskedTensor: ... mask: bool ... value: tf.Tensor ... ... def __tf_flatten__(self): ... metadata = (self.mask,) # static config. ... components = (self.value,) # dynamic values. ... return metadata, components ... ... @classmethod ... def __tf_unflatten__(cls, metadata, components): ... mask = metadata[0] ... value = components[0] ... return MaskedTensor(mask=mask, value=value) ... >>> mt = MaskedTensor(mask=True, value=tf.constant([1])) >>> mt MaskedTensor(mask=True, value=) >>> tf.nest.is_nested(mt) True >>> mt2 = MaskedTensor(mask=False, value=tf.constant([2])) >>> tf.nest.assert_same_structure(mt, mt2) >>> leaves = tf.nest.flatten(mt) >>> leaves [] >>> mt3 = tf.nest.pack_sequence_as(mt, leaves) >>> mt3 MaskedTensor(mask=True, value=) >>> bool(mt == mt3) True >>> tf.nest.map_structure(lambda x: x * 2, mt) MaskedTensor(mask=True, value=) More examples are available in the unit tests (nest_test.py). cy)a Flatten current object into (metadata, components). Returns: A `tuple` of (metadata, components), where - metadata is a custom Python object that stands for the static config of the current object, which is supposed to be fixed and not affected by data transformation. - components is a `tuple` that contains the modifiable fields of the current object. Implementation Note: - This method should not invoke any TensorFlow ops. - This method only needs to flatten the current level. If current object has an attribute that also need custom flattening, nest functions (such as `nest.flatten`) will utilize this method to do recursive flattening. - Components must be a `tuple`, not a `list` N)selfs [/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/util/custom_nest_protocol.py__tf_flatten__z!CustomNestProtocol.__tf_flatten__Scy)aCreate a user-defined object from (metadata, components). Args: metadata: a custom Python object that stands for the static config for reconstructing a new object of the current class. components: a `tuple` that contains the dynamic data fields of the current class, for object reconstruction. Returns: The user-defined object, with the same class of the current object. Implementation Note: - This method should not invoke any TensorFlow ops. - This method only needs to unflatten the current level. If the object has an attribute that also need custom unflattening, nest functions will utilize this method to do recursive unflattening. Nr)clsmetadata componentss r __tf_unflatten__z#CustomNestProtocol.__tf_unflatten__fr r N)__name__ __module__ __qualname____doc__r classmethodrrr r rrs!9v&r r)rtypingrruntime_checkablerrr r rs4 1 aaar