TF_HALF = 19,
  TF_RESOURCE = 20,
  TF_VARIANT = 21,
  TF_UINT32 = 22,
  TF_UINT64 = 23,
  TF_FLOAT8_E5M2 = 24,    // 5 exponent bits, 2 mantissa bits.
  TF_FLOAT8_E4M3FN = 25,  // 4 exponent bits, 3 mantissa bits, finite-only, with
                          // 2 NaNs (0bS1111111).
  // TODO - b/299182407: Leaving room for remaining float8 types.
  // TF_FLOAT8_E4M3FNUZ = 26,
  // TF_FLOAT8_E4M3B11FNUZ = 27,
  // TF_FLOAT8_E5M2FNUZ = 28,

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "RESHAPE"
    }
  ],
  "subgraphs": [
    {
      "tensors": [

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// If input and output tensors don't have names, there shouldn't be an
// `tf.entry_function` attribute created.
// CHECK-NOT: tf.entry_function

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "CONV_2D"
    }
  ],
  "subgraphs": [
    {
      "tensors": [
        {
          "shape": [

	sort.Strings(contents)
	return reflect.DeepEqual(tagContents, contents)
}

func TestManyToManyWithMultiPrimaryKeys(t *testing.T) {
	if name := DB.Dialector.Name(); name == "sqlite" || name == "sqlserver" {
		t.Skip("skip sqlite, sqlserver due to it doesn't support multiple primary keys with auto increment")
	}

	if name := DB.Dialector.Name(); name == "postgres" {
		stmt := gorm.Statement{DB: DB}
		stmt.Parse(&Blog{})

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(%arg0: tensor<*x!quant.uniform<u16:f32, 2.0:37>>) -> tensor<*x!quant.uniform<u16:f32, 2.0:37>> {
// CHECK:     {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ ],
// CHECK-NEXT:  subgraphs: [ {
// CHECK-NEXT:    tensors: [ {
// CHECK-NEXT:      shape: [  ],
// CHECK-NEXT:      type: UINT16,
// CHECK-NEXT:      buffer: 1,

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s
// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - -strip-debug-info | flatbuffer_to_string - | FileCheck %s --check-prefix=STRIP

func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32>
  attributes {tf.entry_function = {inputs = "input", outputs = "SameNameAsOutput"}} {
^bb0(%arg0: tensor<3x2xi32>):
// CHECK: {
// CHECK-NEXT:   version: 3,

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s
// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - -strip-debug-info | flatbuffer_to_string - | FileCheck %s --check-prefix=STRIP

func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32>
  attributes {tf.entry_function = {inputs = "input", outputs = "SameNameAsOutput"}} {
^bb0(%arg0: tensor<3x2xi32>):
// CHECK: {
// CHECK-NEXT:   version: 3,

// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

namespace tflite;

// Here is an overview of the pipeline & runtime metadata looks like:
//
// ======== IRs after TAC ========
// op1 = .... {tac.device = "CPU"}
// op2 = .... {tac.device = "GPU"}
// op3 = .... {tac.device = "GPU"}

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// CHECK: %[[CST:.*]] = "tfl.no_value"() <{value}> : () -> none
// CHECK: %[[RES0:.*]] = "tfl.conv_2d"(%arg0, %arg1, %[[CST]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, none) -> tensor<256x32x32x16xf32>

    if (auto status = tsl::Env::Default()->NewWritableFile(filename, &file);
        !status.ok()) {
      LOG(ERROR) << "Unable to open '" << filename
                 << "' for dumping TFLite MLIR output: " << status;
      return;
    }
    file = std::make_unique<tsl::BufferedWritableFile>(std::move(file));

    WritableFileRawStream os(std::move(file));
    op->print(os);
  }