class_docstr: The tied embeddings layer. 
  See https://arxiv.org/pdf/1608.05859.pdf
  The representations table is taken from a MultichannelLookup layer.
  
  :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 embeddings_layer_name: The name of the layer used for embeddings. 
      Needs to be a MultichannelLookup layer.   
  :type embeddings_layer_name: str
  :param channel: The channel index in the embeddings layer.
  :type channel: int, >=0
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  The layer may have any number of inputs, of the dimensions:
  
  - **BatchLength** * **BatchWidth** * **ListSize** is the number of objects
  - **Height**, **Width**, **Depth** are 1
  - **Channels** is the embedding size
  
  .. rubric:: Layer outputs:
  
  For each input the layer has one output of the same dimensions.
docstr: Sets the channel index in the embeddings layer.
          
code: def channel(self, channel):
      """Sets the channel index in the embeddings layer."""
      if channel < 0:
          raise ValueError("`channel` must be >= 0.")
  
      self._internal.set_channel(channel)
  
file_path: neoml/NeoML/Python/neoml/Dnn/TiedEmbeddings.py
imports: import numpy
  import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
  import neoml.Blob
label_desc: Use image node_id 5 for calling the TiedEmbeddings obj's underlying member method code with example usage: obj.channel(channel) without return types