class_docstr: Class that performs embedding extraction on images.
  
  The API expects a TFLite model with optional, but strongly recommended,
  TFLite Model Metadata.
  
  Input tensor:
    (kTfLiteUInt8/kTfLiteFloat32)
    - image input of size `[batch x height x width x channels]`.
    - batch inference is not supported (`batch` is required to be 1).
    - only RGB inputs are supported (`channels` is required to be 3).
    - if type is kTfLiteFloat32, NormalizationOptions are required to be
      attached to the metadata for input normalization.
  At least one output tensor with:
    (kTfLiteUInt8/kTfLiteFloat32)
    - `N` components corresponding to the `N` dimensions of the returned
      feature vector for this output layer.
    - Either 2 or 4 dimensions, i.e. `[1 x N]` or `[1 x 1 x 1 x N]`.
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docstr: Sends live image data to embedder.
  
  The results will be available via the "result_callback" provided in the
  ImageEmbedderOptions. Embedding extraction is performed on the region of
  interested specified by the `roi` argument if provided, or on the entire
  image otherwise.
  
  Only use this method when the ImageEmbedder is created with the live
  stream running mode. The input timestamps should be monotonically increasing
  for adjacent calls of this method. This method will return immediately after
  the input image is accepted. The results will be available via the
  `result_callback` provided in the `ImageEmbedderOptions`. The
  `embed_async` method is designed to process live stream data such as
  camera input. To lower the overall latency, image embedder may drop the
  input images if needed. In other words, it's not guaranteed to have output
  per input image.
  
  The `result_callback` provides:
    - An embedding result object that contains a list of embeddings.
    - The input image that the image embedder runs on.
    - The input timestamp in milliseconds.
  
  Args:
    image: MediaPipe Image.
    timestamp_ms: The timestamp of the input image in milliseconds.
    image_processing_options: Options for image processing.
  
  Raises:
    ValueError: If the current input timestamp is smaller than what the image
      embedder has already processed.
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code: def embed_async(
      self,
      image: image_module.Image,
      timestamp_ms: int,
      image_processing_options: Optional[
          _ImageProcessingOptions
      ] = None,
  ) -> None:
      """Sends live image data to embedder.
  
      The results will be available via the "result_callback" provided in the
      ImageEmbedderOptions. Embedding extraction is performed on the region of
      interested specified by the `roi` argument if provided, or on the entire
      image otherwise.
  
      Only use this method when the ImageEmbedder is created with the live
      stream running mode. The input timestamps should be monotonically increasing
      for adjacent calls of this method. This method will return immediately after
      the input image is accepted. The results will be available via the
      `result_callback` provided in the `ImageEmbedderOptions`. The
      `embed_async` method is designed to process live stream data such as
      camera input. To lower the overall latency, image embedder may drop the
      input images if needed. In other words, it's not guaranteed to have output
      per input image.
  
      The `result_callback` provides:
        - An embedding result object that contains a list of embeddings.
        - The input image that the image embedder runs on.
        - The input timestamp in milliseconds.
  
      Args:
        image: MediaPipe Image.
        timestamp_ms: The timestamp of the input image in milliseconds.
        image_processing_options: Options for image processing.
  
      Raises:
        ValueError: If the current input timestamp is smaller than what the image
          embedder has already processed.
      """
      normalized_rect = self.convert_to_normalized_rect(
          image_processing_options, image
      )
      self._send_live_stream_data(
          {
              _IMAGE_IN_STREAM_NAME: packet_creator.create_image(
                  image
              ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
              _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
                  normalized_rect.to_pb2()
              ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
          }
      )
  
imports: import dataclasses
  from typing import Callable, Mapping, Optional
  from mediapipe.python import packet_creator
  from mediapipe.python import packet_getter
  from mediapipe.python._framework_bindings import image
  from mediapipe.python._framework_bindings import packet
  from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
  from mediapipe.tasks.cc.components.processors.proto import embedder_options_pb2
  from mediapipe.tasks.cc.vision.image_embedder.proto import image_embedder_graph_options_pb2
  from mediapipe.tasks.python.components.containers import embedding_result
  from mediapipe.tasks.python.components.utils import cosine_similarity
  from mediapipe.tasks.python.core import base_options
  from mediapipe.tasks.python.core import task_info
  from mediapipe.tasks.python.core.optional_dependencies import doc_controls
  from mediapipe.tasks.python.vision.core import base_vision_task_api
  from mediapipe.tasks.python.vision.core import image_processing_options
  from mediapipe.tasks.python.vision.core import vision_task_running_mode
label_desc: Use image node_id 5 for calling the ImageEmbedder obj's underlying member method code with example usage: obj.embed_async(image, timestamp_ms, image_processing_options) without return types