// CHECK-DAG: %[[CST1:.*]] =  "tf.Const"() <{value = dense<[7, 8, 6, 6, 18]> : tensor<5xi64>}> : () -> tensor<5xi64>
  // CHECK: %[[RESHAPE:.*]] = "tf.Reshape"(%arg0, %[[CST]]) : (tensor<1x8x1x1x1x1x1x18xbf16>, tensor<5xi64>) -> tensor<1x8x1x1x18xbf16>
  // CHECK: %[[BROADCAST:.*]] = "tf.BroadcastTo"(%[[RESHAPE]], %[[CST1]]) : (tensor<1x8x1x1x18xbf16>, tensor<5xi64>) -> tensor<7x8x6x6x18xbf16>

  %1 = "tfl.shape"(%conv_s) : (tensor<?x26x26x26x16xf32>) -> tensor<5xi64>
  %2 = "tfl.broadcast_args"(%1, %cst) : (tensor<5xi64>, tensor<1xi64>) -> tensor<5xi64>
  %broad1 = "tfl.broadcast_to"(%conv_s, %2) : (tensor<?x26x26x26x16xf32>, tensor<5xi64>) -> tensor<?x26x26x26x16xf32>
  %broad2 = "tfl.broadcast_to"(%b, %2) : (tensor<16xf32>, tensor<5xi64>) -> tensor<?x26x26x26x16xf32>

func.func @testBroadcastGradientArgI64() -> (tensor<2xi64>, tensor<0xi64>) {
  %s0 = "tf.Const"() {value = dense<> : tensor<0xi64>} : () -> tensor<0xi64>
  %s1 = "tf.Const"() {value = dense<[2, 4]> : tensor<2xi64>} : () -> tensor<2xi64>
  %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) {} : (tensor<0xi64>, tensor<2xi64>) -> (tensor<2xi64>, tensor<0xi64>)

// CHECK-DAG:       %[[VAL_2:.*]] = "tf.Const"() <{value = dense<[1, 0]> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK-DAG:       %[[VAL_3:.*]] = "tf.Const"() <{value = dense<[1, 0]> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK:           %[[VAL_4:.*]] = "tf.Transpose"(%[[VAL_0]], %[[VAL_3]]) : (tensor<2x3xf32>, tensor<2xi64>) -> tensor<3x2xf32>

}

func.func @broadcast_args_i64(%arg0: tensor<3xi64>, %arg1: tensor<1xi64>) -> tensor<3xi64> {
  %0 = "tf.BroadcastArgs"(%arg0, %arg1) : (tensor<3xi64>, tensor<1xi64>) -> tensor<3xi64>
  func.return %0 : tensor<3xi64>

// CHECK-LABEL:broadcast_args_i64
// CHECK:  "tfl.broadcast_args"(%arg0, %arg1) : (tensor<3xi64>, tensor<1xi64>) -> tensor<3xi64>
}

    %cst = arith.constant dense<[2, 1, 200, 200, 200]> : tensor<5xi64>
    %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1x200x200x200xf32>, tensor<1x200x200x200xf32>) -> tensor<2x200x200x200xf32>
    %1 = "tfl.reshape"(%0, %cst) : (tensor<2x200x200x200xf32>, tensor<5xi64>) -> tensor<2x1x200x200x200xf32>
    func.return %1 : tensor<2x1x200x200x200xf32>
  }

  // CHECK:  return %[[CST]]
}

// CHECK-LABEL: @ShapeOpI64
func.func @ShapeOpI64(%arg0 : tensor<576x72xf32>) -> tensor<2xi64> {
  %0 = "tfl.shape"(%arg0) : (tensor<576x72xf32>) -> tensor<2xi64>
  func.return %0 : tensor<2xi64>
  // CHECK: %[[CST:.*]] = arith.constant dense<[576, 72]> : tensor<2xi64>
  // CHECK:  return %[[CST]]
}

// CHECK-LABEL: @ConstFoldStridedSlice

  // CHECK: mhlo.reshape {{.*}} : (tensor<2x1xi64>) -> tensor<2xi64>
  // CHECK: "mhlo.slice"({{.*}}) <{limit_indices = dense<1> : tensor<1xi64>, start_indices = dense<0> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}> : (tensor<2xi64>) -> tensor<1xi64>

  // CHECK-SAME: -> tensor<15xf32>
  // CHECK: %[[LENS:.*]] = "tf.Const"() <{value = dense<3> : tensor<5xi64>}> : () -> tensor<5xi64>
  %concat:2 = "tf.TensorArrayConcatV3"(%ta#0, %ta#1) {element_shape_except0 = #tf_type.shape<*>} : (tensor<!tf_type.resource>, tensor<f32>) -> (tensor<*xf32>, tensor<*xi64>)
  // CHECK: %[[SPLIT_RESHAPE:.*]] = "tf.Reshape"(%[[CONCAT_RESHAPE]],
  // CHECK-SAME: -> tensor<5x3xf32>

  %32 = "tf.ConcatV2"(%29#0, %31, %14) {device = ""} : (tensor<2xi64>, tensor<1xi64>, tensor<i32>) -> tensor<3xi64>
  %33 = "tf.GatherV2"(%24#2, %29#1, %14) {batch_dims = 0 : i64, device = ""} : (tensor<?xi64>, tensor<?xi64>, tensor<i32>) -> tensor<?xi64>
  %34 = "tf.ConcatV2"(%33, %22, %14) {device = ""} : (tensor<?xi64>, tensor<1xi64>, tensor<i32>) -> tensor<?xi64>