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: Performs image classification on the provided video frames.
  
  Only use this method when the ImageClassifier is created with the video
  running mode. It's required to provide the video frame's timestamp (in
  milliseconds) along with the video frame. The input timestamps should be
  monotonically increasing for adjacent calls of this method.
  
  Args:
    image: MediaPipe Image.
    timestamp_ms: The timestamp of the input video frame in milliseconds.
    image_processing_options: Options for image processing.
  
  Returns:
    A classification result object that contains a list of classifications.
  
  Raises:
    ValueError: If any of the input arguments is invalid.
    RuntimeError: If image classification failed to run.
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code: def classify_for_video(
      self,
      image: image_module.Image,
      timestamp_ms: int,
      image_processing_options: Optional[
          _ImageProcessingOptions
      ] = None,
  ) -> ImageClassifierResult:
      """Performs image classification on the provided video frames.
  
      Only use this method when the ImageClassifier is created with the video
      running mode. It's required to provide the video frame's timestamp (in
      milliseconds) along with the video frame. The input timestamps should be
      monotonically increasing for adjacent calls of this method.
  
      Args:
        image: MediaPipe Image.
        timestamp_ms: The timestamp of the input video frame in milliseconds.
        image_processing_options: Options for image processing.
  
      Returns:
        A classification result object that contains a list of classifications.
  
      Raises:
        ValueError: If any of the input arguments is invalid.
        RuntimeError: If image classification failed to run.
      """
      normalized_rect = self.convert_to_normalized_rect(
          image_processing_options, image
      )
      output_packets = self._process_video_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),
          }
      )
  
      classification_result_proto = (
          classifications_pb2.ClassificationResult()
      )
      classification_result_proto.CopyFrom(
          packet_getter.get_proto(
              output_packets[_CLASSIFICATIONS_STREAM_NAME]
          )
      )
  
      return ImageClassifierResult.create_from_pb2(
          classification_result_proto
      )
  
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 4 for calling the ImageClassifier obj's underlying member method code with example usage: obj.classify_for_video(image, timestamp_ms, image_processing_options) and returns: ImageClassifierResult