%3 = "tf.TensorSliceDataset"(%2) {device = "", output_shapes = [#tf_type.shape<>], metadata = ""} : (tensor<3xi32>) -> tensor<*x!tf_type.variant>
  // CHECK: "tf.MapAndBatchDataset"(%[[TSLICE]], %[[BSIZE:.*]], %[[NPC]]
  // CHECK-SAME: f = @"__inference_Dataset_map_<lambda>_80",
  %4 = "tf.MapDataset"(%3) {device = "",
           f = @"__inference_Dataset_map_<lambda>_80",
           output_shapes = [#tf_type.shape<>], output_types = [i32],

  } : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>

  func.return %0 : tensor<!tf_type.variant>
// CHECK: "tf.MapDataset"(
// CHECK-SAME: <{f = @{{.*}}, metadata = "", output_shapes = [#tf_type.shape<>], output_types = [!tf_type.string], preserve_cardinality = true, use_inter_op_parallelism = true}> {Targuments = []}
}

// CHECK-LABEL: TfTakeWhileDataset

def FuseMapAndBatch : Pat<
  (TF_BatchDatasetV2Op
     (TF_MapDatasetOp $input_dataset, $other_arguments, $f, $output_types,
        $output_shapes, $use_inter_op_parallelism, $preserve_cardinality,
        $force_synchronous, $map_dataset_metadata),
     $batch_size, $drop_remainder, $parallel_copy, $batch_output_types,

  %cst = "tf.Const"() <{value = dense<1> : tensor<i32>}> {device = ""} : () -> tensor<i32>
  %0 = "tf.MultiDeviceIteratorFromStringHandle"(%arg0) <{output_shapes = [#tf_type.shape<>], output_types = [!tf_type.string]}> {device = ""} : (tensor<!tf_type.string>) -> tensor<!tf_type.resource>

  return %arg0 : tensor<!tf_type.variant>
}

// 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>

// RUN: tf-tfrt-opt -tf-to-tfrt %s | FileCheck %s --dump-input=fail

// _output_shapes and f.* attributes are removed during tf-to-tfrt lowering.
// CHECK-LABEL: func @remove_unused_attr
func.func @remove_unused_attr() {
  // CHECK: %out_op_chain = tfrt_fallback_async.executeop.seq(%arg0) key(0) cost({{.*}}) device("/device:CPU:0") "tf.SomeOp2"()

    // CHECK: tf_executor.island wraps "tf.VarHandleOp"() <{container = "a", shared_name = "x"}> {_mlir_name = "x", _output_shapes = [#tf_type.shape<>], allowed_devices = [], device = "/device:CPU:0", dtype = i64, shape = #tf_type.shape<>} : () -> tensor<!tf_type.resource<tensor<i64>>>

  %0 = "tf.ReadVariableOp"(%handle) {_output_shapes = ["tfshape$dim { size: 3 }"], device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<3xf32>>>) -> tensor<3xf32>

  std::array<int64_t, 4> output_shape;
  // NHWC <- NCHW
  output_shape[0] = composite_result_shape[0];
  output_shape[1] = composite_result_shape[2];
  output_shape[2] = composite_result_shape[3];
  output_shape[3] = composite_result_shape[1];

  auto input_type = mlir::cast<ShapedType>(old_op->getOperand(0).getType());

  return RankedTensorType::get(output_shape, input_type.getElementType());
}

// Transpose conv supports hybrid computation with quantized weights.
def FoldQuantWeightsIntoTposeConv : Pat<
  (TFL_TransposeConvOp
    $output_shape,
    (TFL_DequantizeOp $quant_weights),
    $quant_input,
    $bias, $padding, $stride_h, $stride_w, $faf),
  (TFL_TransposeConvOp $output_shape, $quant_weights,
    $quant_input, $bias, $padding, $stride_h, $stride_w, $faf),