code: def convert_transpose(params, w_name, scope_name, inputs, layers, weights, names):
      
      print('Converting transpose ...')
      if params['perm'][0] != 0:
          if inputs[0] in layers:
              print('!!! Cannot permute batch dimension. Result may be wrong !!!')
              layers[scope_name] = layers[inputs[0]]
          else:
              print('Skip weight matrix transpose, result may be wrong.')
      else:
          if names:
              tf_name = 'PERM' + random_string(4)
          else:
              tf_name = w_name + str(random.random())
          permute = keras.layers.Permute(params['perm'][1:], name=tf_name)
          layers[scope_name] = permute(layers[inputs[0]])
docs: Convert transpose 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