case ti64*100 + ti16:
			i64 = int64(v); i16 = int16(i64); w = big(i16)
		case ti64*100 + tu16:
			i64 = int64(v); u16 = uint16(i64); w = big(u16)
		case ti64*100 + ti32:
			i64 = int64(v); i32 = int32(i64); w = big(i32)
		case ti64*100 + tu32:
			i64 = int64(v); u32 = uint32(i64); w = big(u32)
		case ti64*100 + ti64:
			i64 = int64(v); i64 = int64(i64); w = big(i64)
		case ti64*100 + tu64:

    tf_device.return %arg0, %arg1, %arg2, %arg3, %arg4 : tensor<i1>, tensor<i32>, tensor<i64>, tensor<f32>, tensor<f64>
  // CHECK-NEXT: }, {
  }, {
    // CHECK-NEXT: tf_device.return
    tf_device.return
  // CHECK-NEXT: }) : () -> (tensor<i1>, tensor<i64>, tensor<f64>)
  }) : () -> (tensor<i1>, tensor<i32>, tensor<i64>, tensor<f32>, tensor<f64>)

	"runtime.gcWriteBarrier7": {Results: []byte{I64}},                                     // -> bufptr
	"runtime.gcWriteBarrier8": {Results: []byte{I64}},                                     // -> bufptr
	"cmpbody":                 {Params: []byte{I64, I64, I64, I64}, Results: []byte{I64}}, // a, alen, b, blen -> -1/0/1

  %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<1xf32>, tensor<f32>, tensor<i32>) -> tensor<1x!tf_type.qint8>
  %1 = "tf.UniformDequantize"(%0, %scales, %zps) {
    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<1x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<1xf32>

    %outputs_0, %control_1 = tf_executor.island wraps "tf.LegacyCall"(%outputs, %outputs) {_disable_call_shape_inference = true, f = @test_func_name0} : (tensor<!tf_type.resource<tensor<i64>>>, tensor<!tf_type.resource<tensor<i64>>>) -> tensor<*x!tf_type.resource> loc("called")

  %1 = "tf.BatchFunction"(%arg0, %0) {allowed_batch_sizes = [6], batch_timeout_micros = 100000 : i64, batching_queue = "", container = "", device = "/device:CPU:0", enable_large_batch_splitting = false, f = @batched_function, max_batch_size = 6 : i64, max_enqueued_batches = 10 : i64, num_batch_threads = 1 : i64, operandSegmentSizes = array<i32: 1, 1>, shared_name = "batch/"} : (tensor<1x3xf32>, tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<*xf32>...

  %2 = "tf.Shape"(%arg0) : (tensor<?x1x2x?xf32>) -> tensor<4xi32>
  %3 = "tf.StridedSlice"(%2, %1, %0, %1) {begin_mask = 0 : i64, ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<4xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>
  func.return %3 : tensor<2xi32>

"tf.BatchFunction"(%cst) <{allowed_batch_sizes = [1], batch_timeout_micros = 5000 : i64, batching_queue = "", container = "", enable_large_batch_splitting = true, f = @_batched, low_priority_allowed_batch_sizes = [], low_priority_batch_timeout_micros = 0 : i64, low_priority_max_batch_size = 0 : i64, low_priority_max_enqueued_batches = 0 : i64, max_batch_size = 1 : i64, max_enqueued_batches = 1 : i64, num_batch_threads = 1 : i64, operandSegmentSizes = array<i32: 1, 0>, shared_name = "batch_function___inf...

// CHECK-SAME:       {program_id = [[PROGRAM_ID:.*]] : i64, variable_arg_indices = []
// CHECK-SAME:       (tensor<1x3xf32>, tensor<3x1xf32>) -> tensor<1x1xf32>
// CHECK-NEXT:    %1 = "tf.Identity"(%arg1) {device = ""} : (tensor<1x3xf32>) -> tensor<1x3xf32>
// CHECK-NEXT:    %2 = "tf.IfrtCall"(%1, %arg0)
// CHECK-SAME:       {program_id = [[PROGRAM_ID]] : i64, variable_arg_indices = []

  %filter = mhlo.constant dense<[[[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]]> : tensor<1x1x3x2xf32>