class ModelWithCustomEncoder(BaseModel):
        dt_field: datetime

        @field_serializer("dt_field")
        def serialize_dt_field(self, dt):
            return dt.replace(microsecond=0, tzinfo=timezone.utc).isoformat()

    class ModelWithCustomEncoderSubclass(ModelWithCustomEncoder):
        pass

    model = ModelWithCustomEncoder(dt_field=datetime(2019, 1, 1, 8))

  EXPECT_EQ(TF_DOUBLE, static_cast<TF_DataType>(tensorflow::DT_DOUBLE));
  EXPECT_EQ(TF_INT32, static_cast<TF_DataType>(tensorflow::DT_INT32));
  EXPECT_EQ(TF_UINT8, static_cast<TF_DataType>(tensorflow::DT_UINT8));
  EXPECT_EQ(TF_INT16, static_cast<TF_DataType>(tensorflow::DT_INT16));
  EXPECT_EQ(TF_INT8, static_cast<TF_DataType>(tensorflow::DT_INT8));

inline bool IsDtypeTrainable(DataType dtype) {
  switch (dtype) {
    case DT_HALF:
    case DT_BFLOAT16:
    case DT_FLOAT:
    case DT_DOUBLE:
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    case DT_RESOURCE:
    case DT_VARIANT:
      return true;
    case DT_QINT8:
    case DT_QINT16:
    case DT_QINT32:
    case DT_QUINT8:
    case DT_QUINT16:
      return tensorflow::flags::Global()

      "    signature {"
      "      name: 'AddVariablesFunction'"
      "      input_arg {"
      "        name: 'var'"
      "        type: DT_RESOURCE"
      "      }"
      "      output_arg {"
      "        name: 'sum'"
      "        type: DT_FLOAT"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'read0'"
      "      op: 'ReadVariableOp'"
      "      input: 'var'"

  AbstractTensorHandlePtr x;
  {
    tracing::TracingTensorHandle* x_raw = nullptr;
    PartialTensorShape shape;
    Status s = dyn_cast<tracing::TracingContext>(ctx.get())->AddParameter(
        DT_FLOAT, shape, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    x.reset(x_raw);
  }

  PartialTensorShape shape;
  Status s = x->Shape(&shape);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

                                }
                                final Date dt = FessFunctions.parseDate(values[0], dateFormate);
                                if (dt != null) {
                                    dataMap.put(mapping.getValue1(), FessFunctions.formatDate(dt));
                                } else {
                                    logger.warn("Failed to parse {}", mapping);

	GOPATH = ""

	// GOROOT - GOROOT value at the time of build.
	GOROOT = ""

	// Version - version time.RFC3339.
	Version = "DEVELOPMENT.GOGET"

	// ReleaseTag - release tag in TAG.%Y-%m-%dT%H-%M-%SZ.
	ReleaseTag = "DEVELOPMENT.GOGET"

	// CommitID - latest commit id.
	CommitID = "DEVELOPMENT.GOGET"

	// ShortCommitID - first 12 characters from CommitID.
	ShortCommitID = "DEVELOPMENT.GOGET"

namespace tensorflow {
namespace {

Status ValidateNonRefOutput(const Node* node, int idx) {
  const DataType& dt = node->output_type(idx);
  return IsRefType(dt)
             ? InvalidArgument("Output ", idx, " of node '", node->name(),
                               "' has a reference type ", DataTypeString(dt))
             : absl::OkStatus();
}

// Converts `ninputs` and `inputs` into `inputs_tensors` and `input_nodes` and

        this.useLocalFesen = useLocalFesen;
        return this;
    }

    public ExecJob remoteDebug() {
        return jvmOptions("-Xdebug", "-Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=localhost:8000");
    }

    public ExecJob gcLogging() {
        final StringBuilder buf = new StringBuilder(100);
        buf.append("-Xlog:gc*,gc+age=trace,safepoint:file=");

  }
}

void ParallelDevice::AsyncWait(TFE_Context* context, TF_Status* status) const {
  StatusPtr first_bad_status(nullptr);

  for (const auto& dt : device_threads_) {
    StatusPtr async_wait_status(TF_NewStatus());
    dt->AsyncWait(async_wait_status.get());
    // Prefer non cancelled errors to uncover real failures.
    if (TF_GetCode(async_wait_status.get()) != TF_OK &&