code: def convert_upsample(params, w_name, scope_name, inputs, layers, weights, names):
      
      print('Converting upsample...')
  
      if params['mode'] != 'nearest':
          raise AssertionError('Cannot convert non-nearest upsampling')
  
      if names == 'short':
          tf_name = 'UPSL' + random_string(4)
      elif names == 'keep':
          tf_name = w_name
      else:
          tf_name = w_name + str(random.random())
  
      if 'height_scale' in params:
          scale = (params['height_scale'], params['width_scale'])
      elif len(inputs) == 2:
          scale = layers[inputs[-1] + '_np'][-2:]
  
      upsampling = keras.layers.UpSampling2D(
          size=scale, name=tf_name
      )
      layers[scope_name] = upsampling(layers[inputs[0]])
docs: Convert nearest upsampling 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