%1 = "tf.ExpandDims"(%0, %cst_0) : (tensor<4x68x68xf32>, tensor<i32>) -> tensor<4x68x68x1xf32>
  %2 = "tf.Conv2D"(%1, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x1xf32>, tensor<5x5x1x1xf32>) -> tensor<4x64x64x1xf32>
  %3 = "tf.Squeeze"(%2) {squeeze_dims = [3]} : (tensor<4x64x64x1xf32>) -> tensor<4x64x64xf32>

func.func @PadStridedSliceNewAxisMask2(%arg0: tensor<4x64x64x1xf32>) -> tensor<1x4x64x64xf32> {
  %cst = arith.constant dense<0> : tensor<3xi32>
  %cst_0 = arith.constant dense<1> : tensor<3xi32>
  %0 = "tf.Squeeze"(%arg0) {T = f32, _output_shapes = ["tfshape$dim { size: 4 } dim { size: 64 } dim { size: 64 }"], device = "", squeeze_dims = []} : (tensor<4x64x64x1xf32>) -> tensor<4x64x64xf32>

  return %1 : tensor<4x8x64x64xf32>
}
func.func private @XlaCallModule_tfl.resize_nearest_neighbor.impl_1(%arg0: tensor<4x8x32x32xf32>) -> tensor<4x8x64x64xf32> {
  %0 = call @XlaCallModule__resize_1(%arg0) : (tensor<4x8x32x32xf32>) -> tensor<4x8x64x64xf32>
  return %0 : tensor<4x8x64x64xf32>
}

  %0 = "tf.ExpandDims"(%arg0, %cst) {device = ""} : (tensor<8x64xf32>, tensor<i32>) -> tensor<8x64x1xf32>
  %1 = "tf.AddV2"(%0, %arg1) {device = ""} : (tensor<8x64x1xf32>, tensor<8x64x64xf32>) -> tensor<8x64x64xf32>
  %2 = "tf.Transpose"(%1, %cst_1) {device = ""} : (tensor<8x64x64xf32>, tensor<3xi32>) -> tensor<64x8x64xf32>
  %3 = "tf.Reshape"(%2, %cst_2) {device = ""} : (tensor<64x8x64xf32>, tensor<2xi32>) -> tensor<512x64xf32>

func.func @reshape_removeIdentity(tensor<4x4x4xf32>) -> tensor<4x4x4xf32> {
^bb0(%arg0: tensor<4x4x4xf32>) :
  %cst = arith.constant dense<[4, 4, 4]> : tensor<3xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<4x4x4xf32>, tensor<3xi32>) -> tensor<4x4x4xf32>
  func.return %0 : tensor<4x4x4xf32>

// CHECK-LABEL: func @reshape_removeIdentity
// CHECK:  return %arg0 : tensor<4x4x4xf32>
}

  func.return %2 : tensor<4x4x4xf32>
}

func.func @testSingleLstmFloat16(%arg0: tensor<4x4xf32>, %arg1: tensor<4xf32>, %arg2: tensor<4x4x4xf32>) -> tensor<4x4x4xf32> {
  // CHECK-LABEL: testSingleLstm
  // CHECK-DAG:  %[[CST_0:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf16>}> : () -> tensor<4x4xf16>

module {
func.func @main(%arg0: tensor<4x68x68x3xf32>, %arg1: tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32> {
  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x3xf32>, tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32>
  func.return %0 : tensor<4x64x64x8xf32>
}

func.func @main(tensor<4x4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>) -> tensor<4x4x4xf32> {
// CHECK: {
// CHECK-NEXT:   version: 3,

    // CHECK: return %[[RESULT]] : tensor<4x4x4xf32>

    %0 = "tf.Atan2"(%arg0, %arg1) : (tensor<4x1xf32>, tensor<4x1x4xf32>) -> tensor<4x4x4xf32>
    func.return %0: tensor<4x4x4xf32>
  }

  // CHECK-LABEL: func @ternary_op
  func.func @ternary_op(%arg0: tensor<2xi1>, %arg1: tensor<2xi32>, %arg2: tensor<2xi32>) -> tensor<2xi32> {
    // CHECK: mhlo.select %arg0, %arg1, %arg2

  %1 = "tf.ReadVariableOp"(%0) : (tensor<!tf_type.resource<tensor<4x4x4xf32>>>) -> tensor<4x4x4xf32>
  "tf_device.cluster_func"(%1) {func = @cluster_func, use_spmd_for_xla_partitioning = true, num_cores_per_replica = 8 : i64} : (tensor<4x4x4xf32>) -> ()
  func.return
}

func.func @cluster_func(%arg0: tensor<4x4x4xf32>) {
  func.return
}

// -----