class_docstr: Class that performs image classification 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 `classes and either 2 or 4 dimensions, i.e. `[1 x N]` or
      `[1 x 1 x 1 x N]`
    - optional (but recommended) label map(s) as AssociatedFiles with type
      TENSOR_AXIS_LABELS, containing one label per line. The first such
      AssociatedFile (if any) is used to fill the `class_name` field of the
      results. The `display_name` field is filled from the AssociatedFile (if
      any) whose locale matches the `display_names_locale` field of the
      `ImageClassifierOptions` used at creation time ("en" by default, i.e.
      English). If none of these are available, only the `index` field of the
      results will be filled.
    - optional score calibration can be attached using ScoreCalibrationOptions
      and an AssociatedFile with type TENSOR_AXIS_SCORE_CALIBRATION. See
      metadata_schema.fbs [1] for more details.
  
  An example of such model can be found at:
  https://tfhub.dev/bohemian-visual-recognition-alliance/lite-model/models/mushroom-identification_v1/1
  
  [1]:
  https://github.com/google/mediapipe/blob/6cdc6443b6a7ed662744e2a2ce2d58d9c83e6d6f/mediapipe/tasks/metadata/metadata_schema.fbs#L456
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docstr: Sends live image data (an Image with a unique timestamp) to perform image classification.
  
  Only use this method when the ImageClassifier 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 `ImageClassifierOptions`. The
  `classify_async` method is designed to process live stream data such as
  camera input. To lower the overall latency, image classifier may drop the
  input images if needed. In other words, it's not guaranteed to have output
  per input image.
  
  The `result_callback` provides:
    - A classification result object that contains a list of classifications.
    - The input image that the image classifier 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
      classifier has already processed.
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code: def classify_async(
      self,
      image: image_module.Image,
      timestamp_ms: int,
      image_processing_options: Optional[
          _ImageProcessingOptions
      ] = None,
  ) -> None:
      """Sends live image data (an Image with a unique timestamp) to perform image classification.
  
      Only use this method when the ImageClassifier 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 `ImageClassifierOptions`. The
      `classify_async` method is designed to process live stream data such as
      camera input. To lower the overall latency, image classifier may drop the
      input images if needed. In other words, it's not guaranteed to have output
      per input image.
  
      The `result_callback` provides:
        - A classification result object that contains a list of classifications.
        - The input image that the image classifier 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
          classifier 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, List
  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 classifications_pb2
  from mediapipe.tasks.cc.components.processors.proto import classifier_options_pb2
  from mediapipe.tasks.cc.vision.image_classifier.proto import image_classifier_graph_options_pb2
  from mediapipe.tasks.python.components.containers import classification_result
  from mediapipe.tasks.python.components.containers import rect
  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 ImageClassifier obj's underlying member method code with example usage: obj.classify_async(image, timestamp_ms, image_processing_options) without return types