}

func.func @FusingbiasAdd(%arg0: tensor<1x10x10x32xf32>, %arg1: tensor<32xf32>) -> tensor<1x10x10x32xf32> {
  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>
  %1 = "tf.BiasAdd"(%0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>

      %outputs_0, %control_1 = tf_executor.island wraps "tf.Identity"(%outputs) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<!tf_type.f32ref>) -> tensor<*xf32>
      tf_executor.fetch %outputs_0 : tensor<*xf32>
    }
    func.return %0 : tensor<*xf32>

    tf_executor.fetch %ro#0, %ro#1 : tensor<*x!tf_type.string>, tensor<*x!tf_type.string>
  }
  return %0#0, %0#1 : tensor<*x!tf_type.string>, tensor<*x!tf_type.string>
}

// -----

//  Serialized string:
//   "\08\01\1A\01\01\22\01\00"
//  Proto debug string:
//   type: MAXIMAL
//   tile_assignment_dimensions: 1
//   tile_assignment_devices: 0

    for (auto result_type : llvm::drop_begin(op->getResultTypes(), 1)) {
      if (!mlir::tf_type::HasCompatibleElementTypes(type, result_type))
        return op->emitOpError()
               << "requires all return types to have compatible element types";
    }
    for (auto operand_type : op->getOperandTypes()) {
      if (!mlir::tf_type::HasCompatibleElementTypes(
              operand_type, type, /*may_ignore_ref_type_lhs=*/true))

func.func @gather(%indices: tensor<?xi32>,
             %resource: tensor<*x!tf_type.resource>) -> tensor<*xi32> {
  %0 = "tf.ResourceGather"(%resource, %indices) {batch_dims = 0 : i64, device = "/device:CPU:0", validate_indices = true}: (tensor<*x!tf_type.resource>, tensor<?xi32>) -> (tensor<*xi32>)
  func.return %0 : tensor<*xi32>
}

  %cst = "tf.Const"() {value = dense<3.000000e-01> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
  %0 = "tf.XlaCallModule"(%arg0, %cst) {
    Sout = [#tf_type.shape<1x2x2x2>], _entry_function = @composite_conv_fn,
    _original_entry_function = "composite_conv_fn",
    _stablehlo_module_attrs = {}, _quantization_method = "weight_only_ptq { }",

    ```mlir
    func.func @single_state_single_dataset_type_no_arguments(%arg0: tensor<!tf_type.variant>, %arg1: tensor<i64>) {
      %0 = "tf.AnonymousIteratorV3"() {output_shapes = [#tf_type.shape<32>], output_types = [f32]} : () -> tensor<!tf_type.resource>
      "tf.MakeIterator"(%arg0, %0) : (tensor<!tf_type.variant>, tensor<!tf_type.resource>) -> ()
      %cst = "tf.Const"() {value = dense<true> : tensor<i1>} : () -> tensor<i1>

    // the same ref type. However, if the output type is a non-ref type T, then
    // the operand can be tensor of type T or T_REF.
    bool is_output_ref = mlir::isa<tf_type::TensorFlowRefType>(
        output_tensor_type.getElementType());
    if (is_output_ref && !mlir::isa<tf_type::TensorFlowRefType>(
                             operand0_tensor_type.getElementType())) {
      return switchn.emitOpError()

    // CHECK:      metadata
    // CHECK:      dtype: DT_RESOURCE
    // CHECK-SAME: kind: VARIABLE

    func.return %0: tensor<*x!tf_type.resource>
  }
  func.func @_func(%arg0: tensor<*x!tf_type.resource>) -> tensor<*x!tf_type.resource> {
    func.return %arg0 : tensor<*x!tf_type.resource>
  }
}

// -----

  func.return %5#3 : tensor<!tf_type.resource>

// CHECK: "tfl.while"
// CHECK: (tensor<i32>, tensor<i32>, tensor<!tf_type.resource>) -> (tensor<i32>, tensor<i32>, tensor<!tf_type.resource>)
}

// CHECK-LABEL: @RemoveFcZeroBias
func.func @RemoveFcZeroBias(%arg0: tensor<1x37xf32>, %arg1: tensor<40x37xf32>) -> tensor<1x40xf32> {