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: Utility function to compute cosine similarity between two embedding entries.
  
  May return an InvalidArgumentError if e.g. the feature vectors are of
  different types (quantized vs. float), have different sizes, or have an
  L2-norm of 0.
  
  Args:
    u: An embedding entry.
    v: An embedding entry.
  
  Returns:
    The cosine similarity for the two embeddings.
  
  Raises:
    ValueError: May return an error if e.g. the feature vectors are of
      different types (quantized vs. float), have different sizes, or have
      an L2-norm of 0.
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code: def cosine_similarity(
      cls,
      u: embedding_result_module.Embedding,
      v: embedding_result_module.Embedding,
  ) -> float:
      """Utility function to compute cosine similarity between two embedding entries.
  
      May return an InvalidArgumentError if e.g. the feature vectors are of
      different types (quantized vs. float), have different sizes, or have an
      L2-norm of 0.
  
      Args:
        u: An embedding entry.
        v: An embedding entry.
  
      Returns:
        The cosine similarity for the two embeddings.
  
      Raises:
        ValueError: May return an error if e.g. the feature vectors are of
          different types (quantized vs. float), have different sizes, or have
          an L2-norm of 0.
      """
      return cosine_similarity.cosine_similarity(u, v)
  
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 6 for calling the ImageEmbedder obj's underlying member method code with example usage: obj.cosine_similarity(cls, u, v) and returns: cosine_similarity