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: Specifies a different initializer for this layer
  than the one set for the whole network in general.
docstr_tok: ["Specifies","a","different","initializer","for","this","layer","than","the","one","set","for","the","whole","network","in","general","."]
code: def initialize(self, initializer):
      """Specifies a different initializer for this layer
      than the one set for the whole network in general.
      """
      if initializer is None:
          return self._internal.clear()
  
      if not isinstance(initializer, Initializer):
          raise ValueError("The `initializer` must be an Initializer.")
  
      self._internal.initialize(initializer._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 6 for calling the MultichannelLookup obj's underlying member method code with example usage: obj.initialize(initializer) and returns: self