code: def convert_squeeze(params, w_name, scope_name, inputs, layers, weights, names):
      
      print('Converting squeeze ...')
  
      if len(params['axes']) > 1:
          raise AssertionError('Cannot convert squeeze by multiple dimensions')
  
      def target_layer(x, axis=int(params['axes'][0])):
          import tensorflow as tf
          return tf.squeeze(x, axis=axis)
  
      lambda_layer = keras.layers.Lambda(target_layer)
      layers[scope_name] = lambda_layer(layers[inputs[0]])
docs: Convert squeeze operation.
  
      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