code: def convert_gather(params, w_name, scope_name, inputs, layers, weights, names):
      
      print('Converting embedding ...')
  
      if names == 'short':
          tf_name = 'EMBD' + random_string(4)
      elif names == 'keep':
          tf_name = w_name
      else:
          tf_name = w_name + str(random.random())
  
      weights_name = '{0}.weight'.format(w_name)
  
      W = weights[weights_name].numpy()
      input_channels, output_channels = W.shape
  
      keras_weights = [W]
  
      dense = keras.layers.Embedding(
          input_channels,
          weights=keras_weights, output_dim=output_channels, name=tf_name
      )
      layers[scope_name] = dense(layers[inputs[1]])
docs: Convert gather (embedding) layer.
  
      Args:
          params: dictionary with layer parameters
          w_name: name prefix in state_dict
          scope_name: pytorch scope name
          inputs: pytorch node inputs
          layers: dictionary with keras tensors
          weights: pytorch state_dict
          names: use short names for keras layers