%2 = "tf.Const"() {__op_key = 2: i32, device = "/device:CPU:0", value = dense<1> : tensor<i64>} : () -> tensor<i64>
  %3 = "tf.RangeDataset"(%0, %1, %2) {__op_key = 3: i32, device = "/device:CPU:0", output_shapes = [#tf_type.shape<>], output_types = [i64], metadata = ""} : (tensor<i64>, tensor<i64>, tensor<i64>) -> tensor<!tf_type.variant>
  // CHECK: tf_mlrt.executeop{{.*}}op: \22FlatMapDataset\22
  // CHECK-SAME: \22__inference_Dataset_flat_map_lambda_19\22

  for (Value v : inputs) operand_types.emplace_back(v.getType());
  llvm::SmallVector<Type, 4> output_types;
  output_types.reserve(outputs.size());
  for (Value v : outputs) output_types.emplace_back(v.getType());

  auto func_type = builder->getFunctionType(operand_types, output_types);

  FuncOp outlined_func =
      FuncOp::create(ops.front()->getLoc(), kHostFunctionAttr, func_type);

mlir::LogicalResult GetOutputTypesForLogicalDeviceComputation(
    int core_id, llvm::ArrayRef<xla::OpSharding> output_sharding_config,
    mlir::tf_device::ClusterFuncOp cluster_func,
    llvm::SmallVectorImpl<mlir::Type>* output_types,
    llvm::SmallVectorImpl<int>* cluster_to_core_index);

// Remaps outputs of `new_parallel_execute` op that represent concurrent
// execution of the `tf_device.cluster_func` at index `cluster_idx` of

  replicas.reserve(num_replicas);
  auto devices = replicate_op.getDevices();

  // Collect result types and operands.
  Operation& terminator = replicate_op.GetBody().back();
  llvm::SmallVector<Type, 8> output_types(terminator.getOperandTypes());
  auto control_type = tf_executor::ControlType::get(island_op.getContext());
  llvm::SmallVector<Value, 8> replica_inputs(island_op.getControlInputs());

      }
      output_types = {new_output_type};
    } else {
      output_types = {output_type.clone(elem_type.getStorageType())};
    }

    SmallVector<Value> args = {q_op.getArg(), scale, zero_point};
    FlatSymbolRefAttr func_name =
        FlatSymbolRefAttr::get(rewriter.getStringAttr(kQuantizeFuncName));

    auto quantize_call = rewriter.create<TF::PartitionedCallOp>(
        loc, output_types, args, func_name,

    mlir::ArrayAttr array_attr, llvm::SmallVector<mlir::Type> &output_types,
    llvm::SmallVector<mlir::DictionaryAttr> &output_attrs) {
  for (auto it : llvm::zip(
           types, array_attr.template getAsRange<mlir::DictionaryAttr>())) {
    if (mlir::isa<tfg::ControlType>(std::get<0>(it))) continue;
    output_types.push_back(std::get<0>(it));

    mlir::NamedAttrList list;

bool IsMustNotBeXlaOp(Operation* op) {
  for (auto& input : op->getOpOperands()) {
    if (TypeMustBeNonXLA(input.get().getType())) return true;
  }
  for (auto output_types : op->getResultTypes()) {
    if (TypeMustBeNonXLA(output_types)) return true;
  }
  return false;
}

// Check if the op must be compiled with XLA. If the op does not satisfy this

}

// CHECK-LABEL: iterators
func.func private @iterators(%arg0 : tensor<!tf_type.variant>) {
  %0 = "tf.Iterator"() {container = "", output_shapes = [#tf_type.shape<200x28x28x1>, #tf_type.shape<200x10>], output_types = [f32, f32], shared_name = "_iterator1"} : () -> tensor<!tf_type.resource>
  %1 = func.call @id(%arg0) : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>

  }

  // CHECK-LABEL: @main
  func.func @main()
  attributes {tf_saved_model.exported_names = ["main"]} {
    %0 = "tf.Iterator"() {container = "", output_shapes = [#tf_type.shape<200x10>], output_types = [f32], shared_name = "foo_iterator"} : () -> tensor<!tf_type.resource>
    %1 = func.call @get_next(%0) : (tensor<!tf_type.resource>) -> tensor<200x10xf32>
    return
  }
}

// -----

        next_func.experimental_ints_on_device = true,
        operandSegmentSizes = array<i32: 2, 2, 1>,
        output_shapes = [#tf_type.shape<?>],
        output_types = [f32],
        metadata = ""
      } :
         (tensor<!tf_type.string>, tensor<*x!tf_type.string>, tensor<!tf_type.string>, tensor<*xi64>, tensor<!tf_type.string>) -> tensor<*x!tf_type.variant>