using ::tensorflow::calibrator::CalibrationStatisticsCollectorMinMax;
using ::tensorflow::calibrator::CalibrationStatisticsMap;

}  // namespace

REGISTER_OP("CalibrationStatisticsSaver")
    .Input("args: Tin")
    .Attr("Tin: list(type) >= 0")
    .Attr("ids: list(string) >= 1")
    .Attr("calibration_methods: list(int) >= 1")
    .Attr("output_file_path: string")
    .SetIsStateful()
    .Doc(R"doc(

  // CHECK: tfrt_fallback_async.batch_function device("/device:CPU:0") @batched_function
  // CHECK-SAME: Tin = [f32]
  // CHECK-SAME: Tout = [f32]
  // CHECK-SAME: allowed_batch_sizes = [6]
  // CHECK-SAME: batch_timeout_micros = 100000 : i64
  // CHECK-SAME: batching_queue = ""
  // CHECK-SAME: container = ""

    DefaultValuedAttr<BoolAttr, "false">:$used_by_host
  );

  let results = (outs
    TF_StrTensor:$array_key,
    TF_Tensor: $tensor_future
  );

  TF_DerivedOperandTypeListAttr Tin = TF_DerivedOperandTypeListAttr<0>;
  TF_DerivedResultTypeListAttr Tout = TF_DerivedResultTypeListAttr<0>;
}


def TF_IfrtCallOp : TF_Op<"IfrtCall", []> {
  let summary = "Invokes a program via IFRT on a device";

  Node* call;
  NameAttrList f_name_attr;
  f_name_attr.set_name(fd.signature().name());
  TF_ASSERT_OK(
      NodeBuilder("B", "StatefulPartitionedCall", &root.graph()->flib_def())
          .Input(inputs)
          .Attr("Tin", {DT_RESOURCE})
          .Attr("Tout", {DT_RESOURCE})
          .Attr("f", f_name_attr)
          .Finalize(root.graph(), &call));
  call->AddAttr(std::string(kMustCompileAttr), true);

	for _, tt := range RunesTests {
		tin := []byte(tt.in)
		a := Runes(tin)
		if !runesEqual(a, tt.out) {
			t.Errorf("Runes(%q) = %v; want %v", tin, a, tt.out)
			continue
		}
		if !tt.lossy {
			// can only test reassembly if we didn't lose information
			s := string(a)
			if s != tt.in {
				t.Errorf("string(Runes(%q)) = %x; want %x", tin, s, tin)
			}
		}
	}
}

  let arguments = (ins
    Variadic<TensorOf<[TF_Float32, TF_Int64]>>:$inputs,

    StrAttr:$output_file_path,
    StrArrayAttr:$ids,
    I32ArrayAttr:$calibration_methods
  );

  TF_DerivedOperandTypeListAttr Tin = TF_DerivedOperandTypeListAttr<0>;
}

def TF_DumpTensorOp : TF_Op<"DumpTensor", []> {
  let summary = "Dump tensor proto.";

  let arguments = (ins
    Arg<TF_Tensor>:$input,

    StrAttr:$log_dir_path,

		ft := (*functype)(unsafe.Pointer(t))
		fv := (*functype)(unsafe.Pointer(v))
		if ft.OutCount != fv.OutCount || ft.InCount != fv.InCount {
			return false
		}
		tin, vin := ft.InSlice(), fv.InSlice()
		for i := 0; i < len(tin); i++ {
			if !typesEqual(tin[i], vin[i], seen) {
				return false
			}
		}
		tout, vout := ft.OutSlice(), fv.OutSlice()
		for i := 0; i < len(tout); i++ {

func.func @remove_unused_attr() {
  // CHECK: %out_op_chain = tfrt_fallback_async.executeop.seq(%arg0) key(0) cost({{.*}}) device("/device:CPU:0") "tf.SomeOp2"()
  "tf.SomeOp2"() {device = "/device:CPU:0", _output_shapes = ["tfshape$"], f.Tin = [f32], f._read_only_resource_inputs = []} : () -> ()
  func.return
}

// CHECK-LABEL: func @basic
func.func @basic(
    %arg0: tensor<3x1xf32>,

</div>

<details markdown="1">
<summary>Về lệnh <code>uvicorn main:app --reload</code>...</summary>

Lệnh `uvicorn main:app` tham chiếu tới những thành phần sau:

* `main`: tệp tin `main.py` (một Python "module").
* `app`: object được tạo trong tệp tin `main.py` tại dòng `app = FastAPI()`.
* `--reload`: chạy lại server sau khi code thay đổi. Chỉ sử dụng trong quá trình phát triển.

</details>

### Kiểm tra

          batch_timeout_micros = 50,
          container = "container",
          shared_name = "shared_name",
          batching_queue = "batching_queue",
          enable_large_batch_splitting = false,
          Tin = [f32],
          Tcaptured = [f32],
          Tout = [f32]} : 1

    Note that the trailing number indicates the number of results.
  }];

  let arguments = (ins
    Variadic<TFTensorType>:$args,