code: def loss(self, *args, target=None, **kwargs):
      r"""Calculates the squared error loss between the observables'
      expectation values and a target.
  
      Keyword Args:
          target (tensor): target values
  
      Returns:
          array[float]: squared error values
      """
  
      if target is None:
          raise ValueError("The target cannot be None")
  
      input_ = self.qnode(*args, **kwargs)
  
      if len(target) != len(input_):
          raise ValueError(
              f"Input target of incorrect length {len(target)} instead of {len(input_)}"
          )
  
      return (input_ - target) ** 2
  
imports: import pennylane
label_desc: Use image node_id 2 for calling the SquaredErrorLoss obj's underlying member method code with example usage: obj.loss() and returns: unknown