ASSERT_EQ(errors::OK, status_.code()) << status_.message();

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

    // Computing numerical gradients with TensorFloat-32 is numerically

Refer to the dashboard [json file here](https://raw.githubusercontent.com/minio/minio/master/docs/metrics/prometheus/grafana/minio-dashboard.json).

![Grafana](https://raw.githubusercontent.com/minio/minio/master/docs/metrics/prometheus/grafana/grafana-minio.png)

            - background
            - raw_headers
            - render
            - init_headers
            - headers
            - set_cookie
            - delete_cookie

::: fastapi.responses.ORJSONResponse
    options:
        members:
            - charset
            - status_code
            - media_type
            - body
            - background
            - raw_headers
            - render

            - background
            - raw_headers
            - render
            - init_headers
            - headers
            - set_cookie
            - delete_cookie

::: fastapi.responses.ORJSONResponse
    options:
        members:
            - charset
            - status_code
            - media_type
            - body
            - background
            - raw_headers
            - render

  {
    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(), 2.0f, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();

  }

  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)
      }
    }

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],