func.return %0 : tensor<1xf32>
  // CHECK: %[[CST:.*]] = arith.constant dense<2.000000e+00> : tensor<1xf32>
  // CHECK:  return %[[CST]]
}

// CHECK-LABEL: @ShapeOpI32
func.func @ShapeOpI32(%arg0 : tensor<576x72xf32>) -> tensor<2xi32> {
  %0 = "tfl.shape"(%arg0) : (tensor<576x72xf32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>

  %2:2 = "tf.IteratorGetNext"(%arg0) {device = "/device:CPU:0"} : (tensor<*x!tf_type.resource>) -> (tensor<128xf32>, tensor<128xf32>)
  %3:2 = "tf.IteratorGetNext"(%arg1) {device = "/device:CPU:0"} : (tensor<*x!tf_type.resource>) -> (tensor<128xf32>, tensor<128xf32>)
  // CHECK: "tf.TPUCompileSucceededAssert"(%[[COMPILE]]#0)
  "tf_device.launch"() ({

// CHECK-LABEL: func @fold_into_mean
func.func @fold_into_mean(%arg0: tensor<1x64x112x112xf32>) -> tensor<1x64xf32> {

  // CHECK: %[[RED_IDX:.*]] = "tf.Const"() <{value = dense<[2, 3]> : tensor<2xi32>}>
  // CHECK: %[[MEAN:[0-9]*]] = "tf.Mean"(%arg0, %[[RED_IDX]])
  // CHECK-SAME: (tensor<1x64x112x112xf32>, tensor<2xi32>) -> tensor<1x64xf32>
  // CHECK: return %[[MEAN]]

  func.return %y : tensor<1x64x28x28xf32>
}

// CHECK-LABEL: bias_add_nchw
func.func @bias_add_nchw(%arg0: tensor<1x256x150x150xf32>, %arg1: tensor<256xf32>) -> tensor<1x256x150x150xf32> {
  // CHECK: (%[[ARG0:.*]]: tensor<1x256x150x150xf32>, %[[ARG1:.*]]: tensor<256xf32>)
  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[R0:.*]] = "tf.Transpose"(%[[ARG0]], %[[CST]])

  func.return %6 : tensor<1x401408xf32>
}

// CHECK-LABEL: quantized_constant
func.func @quantized_constant(%arg0: tensor<1x2xf32>) -> tensor<2x2xf32> {
  %1 = "tfl.quantize"(%arg0) {qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>, volatile} : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<u8:f32, 1.0>>

  %5 = "tf.BiasAdd"(%4, %arg2) : (tensor<1x128x128xf32>, tensor<128xf32>) -> tensor<1x128x128xf32>
  func.return %5 : tensor<1x128x128xf32>

  // CHECK-LABEL: testDilatedConvWithExpandSqueeze1
  // CHECK-SAME: ([[INPUT:%.*]]: tensor<1x128x128xf32>, [[FILTER:%.*]]: tensor<5x5x1x1xf32>, [[BIAS:%.*]]: tensor<128xf32>)
  // CHECK-NEXT: [[AXIS:%.*]] = "tf.Const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>

// NOFALLBACK-LABEL: @xla_svd
func.func @xla_svd(%arg0: tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) {
  // NOFALLBACK: XlaSvd
  %s, %u, %v = "tf.XlaSvd"(%arg0) {max_iter = 1, epsilon = 1.0E-09 : f32, precision_config = ""} : (tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>)
  func.return %s, %u, %v : tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>
}

  // CHECK: %[[OLD_SLICE1:.*]] = "tf.Slice"(%[[READ1]],
  // CHECK: %[[RESHAPE1:.*]] = "tf.Reshape"(%[[VALUE]],
  // CHECK: %[[ADD1:.*]] = "tf.AddV2"(%[[RESHAPE1]], %[[OLD_SLICE1]]) : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<1x3xf32>
  // CHECK: %[[UPDATE1:.*]] = "tf.XlaDynamicUpdateSlice"(%[[READ1]], %[[ADD1]],
  // CHECK: "tf.AssignVariableOp"(%[[GVAR1]], %[[UPDATE1]])

// CHECK:   %[[MUL_0:.*]] = "tf.Mul"(%arg1, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
// CHECK:   return %[[MUL_0]] : tensor<1xf32>
// CHECK: }

  %cst = arith.constant dense<16> : tensor<1xi64>
  %cst_0 = "tfl.no_value"() {value = unit} : () -> none
  %w = arith.constant dense<127.0> : tensor<3x3x3x8x16xf32>
  %b = arith.constant dense<0.0> : tensor<16xf32>