code: def load_embeddings_as_dict(
      path: str,
      embedding_name: str = "default",
      return_all: bool = False,
  ) -> Union[
      Any, Tuple[Any, NDArray[np.float64], List[int], List[str]]
  ]:
      """Loads embeddings from csv and store it in a dictionary for transfer.
  
      Loads embeddings to a dictionary which can be serialized and sent to the
      Lightly servers. It is recommended that the embedding_name is always
      specified because the Lightly web-app does not allow two embeddings with
      the same name.
  
      Args:
          path:
              Path to the csv file.
          embedding_name:
              Name of the embedding for the platform.
          return_all:
              If true, return embeddings, labels, and filenames, too.
  
      Returns:
          A dictionary containing the embedding information (see load_embeddings)
  
      Examples:
          >>> import lightly.utils.io as io
          >>> embedding_dict = io.load_embeddings_as_dict(
          >>>     'path/to/my/embeddings.csv',
          >>>     embedding_name='MyEmbeddings')
          >>>
          >>> result = io.load_embeddings_as_dict(
          >>>     'path/to/my/embeddings.csv',
          >>>     embedding_name='MyEmbeddings',
          >>>     return_all=True)
          >>> embedding_dict, embeddings, labels, filenames = result
  
      """
      embeddings, labels, filenames = load_embeddings(path)
  
      # build dictionary
      data = {
          "embeddingName": embedding_name,
          "embeddings": [
              {
                  "fileName": filename,
                  "value": embedding.tolist(),
                  "label": label,
              }
              for embedding, filename, label in zip(
                  embeddings, filenames, labels
              )
          ],
      }
  
      # return embeddings along with dictionary
      if return_all:
          return data, embeddings, labels, filenames
      else:
          return data
  
file_path: lightly/lightly/utils/io.py
imports: import csv
  import json
  import re
  from itertools import compress
  from typing import Any, Dict, List, Tuple, Union
  import numpy
  from numpy.typing import NDArray
label_desc: Use image node_id 4 for calling a global function with example usage: load_embeddings_as_dict(path, embedding_name, return_all) and returns: data,  embeddings,  labels,  filenames, data