code: def convert_concat(params, w_name, scope_name, inputs, layers, weights, names):
      
      print('Converting concat ...')
      concat_nodes = [layers[i] for i in inputs]
  
      if len(concat_nodes) == 1:
          # no-op
          layers[scope_name] = concat_nodes[0]
          return
  
      if names == 'short':
          tf_name = 'CAT' + random_string(5)
      elif names == 'keep':
          tf_name = w_name
      else:
          tf_name = w_name + str(random.random())
  
      cat = keras.layers.Concatenate(name=tf_name, axis=params['axis'])
      layers[scope_name] = cat(concat_nodes)
docs: Convert concatenation.
  
      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