// CHECK:           return %[[VAL_2]] : tensor<1x4xi8>
// CHECK:         }
func.func @bitwise_or_broadcast_chlo(%arg0: tensor<1xi8>, %arg1: tensor<1x4xi8>) -> tensor<1x4xi8> {
  %0 = "chlo.broadcast_or"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<1xi8>, tensor<1x4xi8>) -> tensor<1x4xi8>
  func.return %0 : tensor<1x4xi8>
}

// CHECK-LABEL:   func @bitwise_or_dynamic(

}

func.func @DontConvertIdentityScatterNdWithLargerOutputDimSize(%arg0: tensor<1xf32>) -> tensor<2xf32> {
  %cst = arith.constant dense<[[0]]> : tensor<1x1xi32>
  %shape = arith.constant dense<[2]> : tensor<1xi32>
  %0 = "tfl.scatter_nd"(%cst, %arg0, %shape) : (tensor<1x1xi32>, tensor<1xf32>, tensor<1xi32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>

// CHECK-LABEL: DontConvertIdentityScatterNdWithLargerOutputDimSize

func.func @all_keep_dim(%input: tensor<4x8xi1>) -> tensor<4x1xi1> {
  // CHECK: mhlo.reshape %{{.*}} : (tensor<4xi1>) -> tensor<4x1xi1>
  %dims = "tf.Const"() { value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %0 = "tf.All"(%input, %dims) {keep_dims = true} : (tensor<4x8xi1>, tensor<1xi32>) -> tensor<4x1xi1>
  func.return %0 : tensor<4x1xi1>
}

// -----

// CHECK-LABEL: @all_dynamic

  %20 = "tf.ExpandDims"(%19, %7) {device = ""} : (tensor<1xi32>, tensor<i32>) -> tensor<1x1xi32>
  %21 = "tf.Cast"(%20) {Truncate = false, device = ""} : (tensor<1x1xi32>) -> tensor<1x1xi64>
  %22 = "tf.Reshape"(%21, %12) {device = ""} : (tensor<1x1xi64>, tensor<1xi64>) -> tensor<1xi64>
  %23 = "tf.Reshape"(%arg0, %5) {device = ""} : (tensor<1x!tf_type.string>, tensor<1xi32>) -> tensor<1x!tf_type.string>