func.func @valid_unranked_inputs_on_reshape(%arg0: tensor<3x4xi32>, %arg1: tensor<*xi32>) -> tensor<3x4xi32> {
  // CHECK: "tfl.reshape"(%arg0, %arg1)
  %0 = "tfl.reshape"(%arg0, %arg1) : (tensor<3x4xi32>, tensor<*xi32>) -> tensor<3x4xi32>
  func.return %0 : tensor<3x4xi32>
}

// -----

// CHECK-LABEL: valid_one_dynamic_dim_on_reshape
func.func @valid_one_dynamic_dim_on_reshape(%arg0: tensor<3x4xi32>) -> tensor<1x3x4xi32> {

// CHECK-LABEL: func @tensorProtoAttr
func.func @tensorProtoAttr() -> () {
^bb0:
// CHECK: "tf.TensorProtoIntTensor"() {bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x1x4xi32>} : () -> ()
  "tf.TensorProtoIntTensor"(){bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x1x4xi32>} : () -> ()
// CHECK: "tf.TensorProtoFloatTensor"() {bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x1x4xf32>} : () -> ()

}

// Returns a RankedTensorType which is similar to `input_type` but replaces the
// dimension size of `dim` with `dim_size`.  For example,
// `SubstituteRankedTensorTypeDimSize(tensor<3x4xi32>, 1, 2)` returns
// `tensor<3x2xi32>`.
static RankedTensorType SubstituteRankedTensorTypeDimSize(
    RankedTensorType input_type, int64_t dim, int64_t dim_size) {
  auto shape = input_type.getShape().vec();