|---| | [ View source on GitHub](https://github.com/tensorflow/tflite-support/blob/v0.4.4/tensorflow_lite_support/python/task/processor/proto/embedding_options.proto) | Options for embedding processor. tflite_support.task.processor.EmbeddingOptions( l2_normalize: Optional[bool] = None, quantize: Optional[bool] = None )


| ## Attributes || |---|---| | `l2_normalize` | Whether to normalize the returned feature vector with L2 norm. Use this option only if the model does not already contain a native L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and L2 norm is thus achieved through TF Lite inference. | | `quantize` | Whether the returned embedding should be quantized to bytes via scalar quantization. Embeddings are implicitly assumed to be unit-norm and therefore any dimension is guaranteed to have a value in \[-1.0, 1.0\]. Use the l2_normalize option if this is not the case. |
## Methods ### `create_from_pb2` [View source](https://github.com/tensorflow/tflite-support/blob/v0.4.4/tensorflow_lite_support/python/task/processor/proto/embedding_options.proto) @classmethod create_from_pb2( pb2_obj: _EmbeddingOptionsProto ) -> 'EmbeddingOptions' Creates a `EmbeddingOptions` object from the given protobuf object. ### `to_pb2` [View source](https://github.com/tensorflow/tflite-support/blob/v0.4.4/tensorflow_lite_support/python/task/processor/proto/embedding_options.proto) to_pb2() -> _EmbeddingOptionsProto Generates a protobuf object to pass to the C++ layer. ### `__eq__` [View source](https://github.com/tensorflow/tflite-support/blob/v0.4.4/tensorflow_lite_support/python/task/processor/proto/embedding_options.proto) __eq__( other: Any ) -> bool Checks if this object is equal to the given object.


| Args || |---|---| | `other` | The object to be compared with. |



| Returns || |---|---| | True if the objects are equal. ||



| ## Class Variables || |---|---| | l2_normalize | `None` | | quantize | `None` |