}

mlir::LogicalResult IslandOp::verify() {
  IslandOp island = *this;
  if (!island.GetBody().args_empty())
    return island.emitOpError() << "expects body without any arguments";

  Operation &yield = island.GetBody().back();
  if (!isa<YieldOp>(yield))
    return yield.emitOpError()
           << "invalid tf_executor.island terminator, yield expected";

  func.func @main() {
    tf_executor.graph {
      %control = tf_executor.island {
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    tf_executor.graph {
      %control = tf_executor.island {
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    return
  }

	AFCLASSS

	// 12.3: Double-Precision Load and Store Instructions
	AFLD
	AFSD

	// 12.4: Double-Precision Floating-Point Computational Instructions
	AFADDD
	AFSUBD
	AFMULD
	AFDIVD
	AFMIND
	AFMAXD
	AFSQRTD
	AFMADDD
	AFMSUBD
	AFNMADDD
	AFNMSUBD

	// 12.5: Double-Precision Floating-Point Conversion and Move Instructions
	AFCVTWD
	AFCVTLD
	AFCVTDW
	AFCVTDL
	AFCVTWUD

    tf_executor.graph {
      %control = tf_executor.island {
        "tf.StatefulPartitionedCall"(%arg0) <{config = "", config_proto = "", executor_type = "", f = @partitioned}> : (tensor<*x!tf_type.resource>) -> ()
        tf_executor.yield
      }

        %0 = tf_executor.graph {
          %outputs, %control = tf_executor.island wraps "tf.XlaSharding"(%arg0) {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\01", sharding = "\08\03\1A\02\02\01\22\02\00\01"} : (tensor<2x2xf32>) -> tensor<2x2xf32>

	}

	AssertAssociationCount(t, user2, "Team", 0, "after clear")
}

func TestHasManyAssociationForSlice(t *testing.T) {
	users := []User{
		*GetUser("slice-hasmany-1", Config{Pets: 2}),
		*GetUser("slice-hasmany-2", Config{Pets: 0}),
		*GetUser("slice-hasmany-3", Config{Pets: 4}),
	}

	DB.Create(&users)

	// Count
	AssertAssociationCount(t, users, "Pets", 6, "")

	// Find
	var pets []Pet

          this->join(getLatticeElement(result), *getLatticeElement(arg.get()));
        }
      }
    } else if (auto island = dyn_cast<tf_executor::IslandOp>(op)) {
      for (auto &arg : island.GetYield()->getOpOperands()) {
        auto result = island.getResult(arg.getOperandNumber());
        this->join(getLatticeElement(result), *getLatticeElement(arg.get()));
      }
    } else {
      return false;

    // the function body or if they are nested in individual
    // `tf_executor.island`.
    for (Block& block : func.getBody()) BuildClusters(block, builder, info);
    func.walk([&](tf_executor::IslandOp island) {
      BuildClusters(island.GetBody(), builder, info);
    });
  }
}

}  // namespace

)

// GenerateDeltas computes Workload resources. This is design to be highly optimized to delta updates,
// and supports *on-demand* client usage. A client can subscribe with a wildcard subscription and get all
// resources (with delta updates), or on-demand and only get responses for specifically subscribed resources.
//

	ADARN
	AMADDHD
	AMADDHDU
	AMADDLD

	/* Vector */
	ALVEBX
	ALVEHX
	ALVEWX
	ALVX
	ALVXL
	ALVSL
	ALVSR
	ASTVEBX
	ASTVEHX
	ASTVEWX
	ASTVX
	ASTVXL
	AVAND
	AVANDC
	AVNAND
	AVOR
	AVORC
	AVNOR
	AVXOR
	AVEQV
	AVADDUM
	AVADDUBM
	AVADDUHM
	AVADDUWM
	AVADDUDM
	AVADDUQM
	AVADDCU
	AVADDCUQ
	AVADDCUW
	AVADDUS
	AVADDUBS