class_docstr: The layer that trains fixed-length vector representation for the values
  of several discrete features.
  See https://en.wikipedia.org/wiki/Word2vec, 
  https://en.wikipedia.org/wiki/GloVe_(machine_learning)
  
  :param input_layers: The input layers to be connected. 
      The integer in each tuple specifies the number of the output.
      If not set, the first output will be used.
  :type input_layers: object, tuple(object, int) or list of them
  :param dimensions: Each of the elements specifies the number and length of vectors 
      in the representation table with the element's index.
  :type dimensions: list of tuple(int, int)
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a blob with feature values (float or int).
      The dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** * **Height** * **Width** * **Depth**
        is the number of features
      - **Channels** is the dimension along which the feature values for
        different sets are stored. Not smaller than the number of 
        feature sets.
      
  .. rubric:: Layer outputs:
  
  (1) the blob with vectors for each input feature.
      The dimensions:
  
      - **BatchLength**, **BatchWidth**, **ListSize**, **Height**, **Width**, **Depth**
        are the same as for the input
      - **Channels** is the sum of vector lengths of all sets and additional channels
        if the input **Channels** is more than the number of tables.
docstr: Sets the representation table with the given index 
  as a blob of the dimensions:
  
      - BatchLength * BatchWidth * ListSize is dimensions[i].VectorCount
      - Height * Width * Depth * Channels is dimensions[i].VectorSize
docstr_tok: ["Sets","the","representation","table","with","the","given","index","as","a","blob","of","the","dimensions",":","-","BatchLength","*","BatchWidth","*","ListSize","is","dimensions","[","i","]",".VectorCount","-","Height","*","Width","*","Depth","*","Channels","is","dimensions","[","i","]",".VectorSize"]
code: def set_embeddings(self, index, blob):
      """Sets the representation table with the given index
      as a blob of the dimensions:
  
          - BatchLength * BatchWidth * ListSize is dimensions[i].VectorCount
          - Height * Width * Depth * Channels is dimensions[i].VectorSize
      """
      if not type(blob) is Blob.Blob:
          raise ValueError("The `blob` must be neoml.Blob.")
  
      return self._internal.set_embeddings(index, blob._internal)
  
file_path: neoml/NeoML/Python/neoml/Dnn/MultichannelLookup.py
imports: import numpy
  import neoml.PythonWrapper
  from .Dnn import Layer
  from .Initializer import Initializer
  from neoml.Utils import check_input_layers
  import neoml.Blob
label_desc: Use image node_id 5 for calling the MultichannelLookup obj's underlying member method code with example usage: obj.set_embeddings(index, blob) and returns: self