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: Creates an `ImageEmbedder` object from a TensorFlow Lite model and the default `ImageEmbedderOptions`.
  
  Note that the created `ImageEmbedder` instance is in image mode, for
  embedding image on single image inputs.
  
  Args:
    model_path: Path to the model.
  
  Returns:
    `ImageEmbedder` object that's created from the model file and the default
    `ImageEmbedderOptions`.
  
  Raises:
    ValueError: If failed to create `ImageEmbedder` object from the provided
      file such as invalid file path.
    RuntimeError: If other types of error occurred.
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code: def create_from_model_path(cls, model_path: str) -> "ImageEmbedder":
      """Creates an `ImageEmbedder` object from a TensorFlow Lite model and the default `ImageEmbedderOptions`.
  
      Note that the created `ImageEmbedder` instance is in image mode, for
      embedding image on single image inputs.
  
      Args:
        model_path: Path to the model.
  
      Returns:
        `ImageEmbedder` object that's created from the model file and the default
        `ImageEmbedderOptions`.
  
      Raises:
        ValueError: If failed to create `ImageEmbedder` object from the provided
          file such as invalid file path.
        RuntimeError: If other types of error occurred.
      """
      base_options = _BaseOptions(model_asset_path=model_path)
      options = ImageEmbedderOptions(
          base_options=base_options, running_mode=_RunningMode.IMAGE
      )
      return cls.create_from_options(options)
  
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 1 for calling the ImageEmbedder obj's underlying member method code with example usage: obj.create_from_model_path(cls, model_path) and returns: cls