}

  private fun serveConnection(raw: Socket) {
    taskRunner.newQueue().execute("MockWebServer ${raw.remoteSocketAddress}", cancelable = false) {
      try {
        SocketHandler(raw).handle()
      } catch (e: IOException) {
        logger.fine("$this connection from ${raw.inetAddress} failed: $e")
      } catch (e: Exception) {
        logger.log(Level.SEVERE, "$this connection from ${raw.inetAddress} crashed", e)
      }
    }

  {
    AbstractTensorHandle* x_raw = nullptr;
    Status s =
        TestScalarTensorHandle<float, TF_FLOAT>(ctx_.get(), 2.0f, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    x.reset(x_raw);
  }

  AbstractTensorHandlePtr y;
  {
    AbstractTensorHandle* y_raw = nullptr;
    Status s =
        TestScalarTensorHandle<float, TF_FLOAT>(ctx_.get(), 7.0f, &y_raw);

func NewChecksumFromData(t ChecksumType, data []byte) *Checksum {
	if !t.IsSet() {
		return nil
	}
	h := t.Hasher()
	h.Write(data)
	raw := h.Sum(nil)
	c := Checksum{Type: t, Encoded: base64.StdEncoding.EncodeToString(raw), Raw: raw}
	if !c.Valid() {
		return nil
	}
	return &c
}

// ReadCheckSums will read checksums from b and return them.

  {
    AbstractContext* ctx_raw = nullptr;
    Status s =
        BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    ctx.reset(ctx_raw);
  }

  AbstractTensorHandlePtr t;
  {
    AbstractTensorHandle* x_raw = nullptr;
    Status s = TestScalarTensorHandle<float, TF_FLOAT>(ctx.get(), 1.0f, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();

  AbstractTensorHandlePtr x1;
  {
    AbstractTensorHandle* x1_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 1.0f, &x1_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    x1.reset(x1_raw);
  }

  AbstractTensorHandlePtr x2;
  {
    AbstractTensorHandle* x2_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(

    int64_t vals_shape[] = {num_dims_out};
    Range(&vals, 0, num_dims_out);
    AbstractTensorHandle* sum_dims_raw = nullptr;
    TF_RETURN_IF_ERROR(TestTensorHandleWithDims<int32_t, TF_INT32>(
        ctx, vals.data(), vals_shape, 1, &sum_dims_raw));
    sum_dims.reset(sum_dims_raw);
  }

  // Reduce sum the output on all dimensions.
  TF_RETURN_IF_ERROR(ops::Sum(ctx, model_out.get(), sum_dims.get(), &outputs[0],

from fastapi import FastAPI, Request

app = FastAPI()


def magic_data_reader(raw_body: bytes):
    return {
        "size": len(raw_body),
        "content": {
            "name": "Maaaagic",
            "price": 42,
            "description": "Just kiddin', no magic here. ✨",
        },
    }


@app.post(
    "/items/",
    openapi_extra={
        "requestBody": {
            "content": {

  {
    AbstractContext* ctx_raw = nullptr;
    Status s =
        BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    ctx.reset(ctx_raw);
  }

  AbstractTensorHandlePtr x;
  {
    AbstractTensorHandle* x_raw = nullptr;
    Status s = TestScalarTensorHandle<float, TF_FLOAT>(ctx.get(), 1.0f, &x_raw);

//
// +k8s:deepcopy-gen=true
// +protobuf=true
// +k8s:openapi-gen=true
message RawExtension {
  // Raw is the underlying serialization of this object.
  //
  // TODO: Determine how to detect ContentType and ContentEncoding of 'Raw' data.
  optional bytes raw = 1;
}

// TypeMeta is shared by all top level objects. The proper way to use it is to inline it in your type,
// like this:
//

        Hashing.hmacSha1(keyData).toString());

    assertEquals(
        "Hashing.hmacSha256(Key[algorithm=HmacSHA256, format=RAW])",
        Hashing.hmacSha256(SHA256_KEY).toString());
    assertEquals(
        "Hashing.hmacSha256(Key[algorithm=HmacSHA256, format=RAW])",
        Hashing.hmacSha256(keyData).toString());

    assertEquals(
        "Hashing.hmacSha512(Key[algorithm=HmacSHA512, format=RAW])",