- Kubeadm: add the deprecated flag --port=0 to kube-controller-manager and kube-scheduler manifests to disable insecure serving. Without this flag the components by default serve (e.g. /metrics) insecurely on the default node interface (controlled by --address). Users that wish to override this behavior and enable insecure serving can pass a custom --port=X via kubeadm's "extraArgs" mechanic for these components. ([#92720](https://github.com/kubernetes/kubernetes/pull/92720),...

*   `Estimator.export_savedmodel()` now includes all valid serving signatures
    that can be constructed from the Serving Input Receiver and all available
    ExportOutputs. For instance, a classifier may provide regression- and
    prediction-flavored outputs, in addition to the classification-flavored one.
    Building signatures from these allows TF Serving to honor requests using the

                  status condition will be true.


                  A Gateway for this GatewayClass may provide its own `parametersRef`. When both are specified,
                  the merging behavior is implementation specific.
                  It is generally recommended that GatewayClass provides defaults that can be overridden by a Gateway.


                  Support: Implementation-specific

out of the beam MO, except the least refrangible, which at T appear of a
full red, their Colour is the same in that beam MO as afterwards at T,
the Refraction of the Prism HJK serving only to separate the differently
refrangible Rays, without making any Alteration in their Colours, as
shall be more fully proved hereafter. All which confirms as well the
first Proposition as the second.

            dim {
              size: 150
            }
            dim {
              size: 150
            }
          }
        }
      }
      method_name: "tensorflow/serving/predict"
    }
  }
  object_graph_def {
    nodes {
      children {
        node_id: 1
        local_name: "get_c"
      }
      children {
        node_id: 2

    // Gets the argument operands to the called function.
    operand_range getArgOperands() {
      // Merge `in_tensors` and `captured_tensors`. `operandSegmentSizes`
      // has two elements, so it will be merging two operand ranges (the two
      // variadic arguments). Attributes like `batch_timeout_micros` will not
      // make it to the return array.