The op serializes protobuf messages provided in the input tensors.

The types of the tensors in values must match the schema for the fields specified in field_names. All the tensors in values must have a common shape prefix, batch_shape.

The sizes tensor specifies repeat counts for each field. The repeat count (last dimension) of a each tensor in values must be greater than or equal to corresponding repeat count in sizes.

A message_type name must be provided to give context for the field names. The actual message descriptor can be looked up either in the linked-in descriptor pool or a filename provided by the caller using the descriptor_source attribute.

For the most part, the mapping between Proto field types and TensorFlow dtypes is straightforward. However, there are a few special cases:

  • A proto field that contains a submessage or group can only be converted to DT_STRING (the serialized submessage). This is to reduce the complexity of the API. The resulting string can be used as input to another instance of the decode_proto op.

  • TensorFlow lacks support for unsigned integers. The ops represent uint64 types as a DT_INT64 with the same twos-complement bit pattern (the obvious way). Unsigned int32 values can be represented exactly by specifying type DT_INT64, or using twos-complement if the caller specifies DT_INT32 in the output_types attribute.

The descriptor_source attribute selects the source of protocol descriptors to consult when looking up message_type. This may be:

  • An empty string or "local://", in which case protocol descriptors are created for C++ (not Python) proto definitions linked to the binary.

  • A file, in which case protocol descriptors are cre