tf.nest.pack_sequence_as

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Returns a given flattened sequence packed into a given structure.

View aliases

Main aliases

`tf.contrib.framework.nest.pack_sequence_as`

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.nest.pack_sequence_as, `tf.compat.v2.nest.pack_sequence_as`

tf.nest.pack_sequence_as(
    structure, flat_sequence, expand_composites=False
)

If structure is a scalar, flat_sequence must be a single-element list; in this case the return value is flat_sequence[0].

If structure is or contains a dict instance, the keys will be sorted to pack the flat sequence in deterministic order. This is true also for OrderedDict instances: their sequence order is ignored, the sorting order of keys is used instead. The same convention is followed in flatten. This correctly repacks dicts and OrderedDicts after they have been flattened, and also allows flattening an OrderedDict and then repacking it back using a corresponding plain dict, or vice-versa. Dictionaries with non-sortable keys cannot be flattened.

Args
structure	Nested structure, whose structure is given by nested lists, tuples, and dicts. Note: numpy arrays and strings are considered scalars.
flat_sequence	flat sequence to pack.
expand_composites	If true, then composite tensors such as tf.SparseTensor and tf.RaggedTensor are expanded into their component tensors.

Returns
packed	flat_sequence converted to have the same recursive structure as structure.

Raises
ValueError	If flat_sequence and structure have different element counts.
TypeError	structure is or contains a dict with non-sortable keys.