code: def sample_ising(self, linear, quadratic, **params):
          
          # Our linear and quadratic objective terms are already separated in an
          # ising model so we can just directly call `_sample`.
          return self._sample('ising', linear, quadratic, params)
docs: Sample from the specified Ising model.
  
          Args:
              linear (list/dict): Linear terms of the model (h).
              quadratic (dict of (int, int):float): Quadratic terms of the model (J).
              **params: Parameters for the sampling method, specified per solver.
  
          Returns:
              :obj:`Future`
  
          Examples:
              This example creates a client using the local system's default D-Wave Cloud Client
              configuration file, which is configured to access a D-Wave 2000Q QPU, submits a
              simple :term:`Ising` problem (opposite linear biases on two coupled qubits), and samples
              5 times.
  
              >>> from dwave.cloud import Client
              >>> with Client.from_config() as client:
              ...     solver = client.get_solver()
              ...     u, v = next(iter(solver.edges))
              ...     computation = solver.sample_ising({u: -1, v: 1},{}, num_reads=5)   # doctest: +SKIP
              ...     for i in range(5):
              ...         print(computation.samples[i][u], computation.samples[i][v])
              ...
              ...
              (1, -1)
              (1, -1)
              (1, -1)
              (1, -1)
              (1, -1)