// This ensures that constants of different kind but equal value (such as
// 1.0 + 0i, 1.0, 1) result in the same value.
func keyVal(x constant.Value) interface{} {
	switch x.Kind() {
	case constant.Complex:
		f := constant.ToFloat(x)
		if f.Kind() != constant.Float {
			r, _ := constant.Float64Val(constant.Real(x))
			i, _ := constant.Float64Val(constant.Imag(x))
			return complex(r, i)
		}
		x = f
		fallthrough

		for _, delKey := range strings.Fields(inputs[1]) {
			_, ok := currKVS.Lookup(delKey)
			if !ok {
				return Error[ErrConfigNotFound]("key %s doesn't exist", delKey)
			}
			defVal, isDef := defKVS.Lookup(delKey)
			if isDef {
				currKVS.Set(delKey, defVal)
			} else {
				currKVS.Delete(delKey)
			}
		}
		c[subSys][tgt] = currKVS
	} else {
		delete(c[subSys], tgt)
	}
	return nil
}

    mlir::OperationName name = inner_op->getName();
    if (!name.isRegistered() &&
        // Skip unmodelled ops that are handled differently.
        (node_type_name != "_Arg" && node_type_name != "_Retval") &&
        !unmodelled_op_names_.count(name.getIdentifier())) {
      if (node.op_def().is_stateful()) {
        VLOG(1) << "[potentially conservative] Op type `" << node.type_string()

      "XlaVariadicReduce",
      "XlaVariadicReduceV2",
      "XlaVariadicSort",
      "XlaWhile",
      "Zeta",
      "_Arg",
      "_ArrayToList",
      "_ListToArray",
      "_Retval"};
  return result;
}

}  // namespace testing

//       }
// .   }
//     kind: PARAMETER
//     sharding {
//       type: MAXIMAL
//       tile_assignment_dimensions: 1
//       tile_assignment_devices: 0
//     }
//   }
//   retvals {
//     sharding {
//       type: MAXIMAL
//       tile_assignment_dimensions: 1
//       tile_assignment_devices: 0
//     }
//   }
//   num_replicas: 1
//   num_cores_per_replica: 1

  %1 = "tf.Imag"(%arg0) : (tensor<3xcomplex<f32>>) -> tensor<3xf32>
  func.return %1 : tensor<3xf32>
}

// -----

// CHECK-LABEL: func @real
func.func @real(%arg0: tensor<3xcomplex<f32>>) -> tensor<3xf32> {
  // CHECK: mhlo.real
  %1 = "tf.Real"(%arg0) : (tensor<3xcomplex<f32>>) -> tensor<3xf32>
  func.return %1 : tensor<3xf32>
}

  std::string args_communication_key =
      llvm::formatv("host_compute_channel_{0}_args", (communication_key_index))
          .str();
  std::string retvals_communication_key =
      llvm::formatv("host_compute_channel_{0}_retvals",
                    (communication_key_index))
          .str();

  // Use a unique name when sending just the IfRegion predicate.  This is
  // for readable and to match the key in the TF2XLA bridge.

			t.Errorf("MapIter.Key() = %q, MapIter.SetKey() = %q", key, setkey)
		}
		if setval, val := valueToString(e), valueToString(iter.Value()); setval != val {
			t.Errorf("MapIter.Value() = %q, MapIter.SetValue() = %q", val, setval)
		}
	}

	if testenv.OptimizationOff() {
		return // no inlining with the noopt builder
	}

  // CHECK-LABEL: @const_input_required
  func.func @const_input_required(%arg0: tensor<10xf64>) -> tensor<?xf64> attributes {tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2,_arg3", outputs = "_retval0"}} {
    %cst = "tf.Const"() {value = dense<6> : tensor<1xi32>} : () -> tensor<1xi32>
    %cst_0 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>

		// defer is set.
		bitval := s.constInt8(types.Types[types.TUINT8], 1<<uint(i))
		andval := s.newValue2(ssa.OpAnd8, types.Types[types.TUINT8], deferBits, bitval)
		eqVal := s.newValue2(ssa.OpEq8, types.Types[types.TBOOL], andval, zeroval)
		b := s.endBlock()
		b.Kind = ssa.BlockIf
		b.SetControl(eqVal)
		b.AddEdgeTo(bEnd)
		b.AddEdgeTo(bCond)
		bCond.AddEdgeTo(bEnd)