apiVersion: networking.istio.io/v1alpha3
kind: ServiceEntry
metadata:
  name: proxy-service-instance
spec:
  hosts:
  - example.com
  ports:
  - number: 7070
    name: auto
    protocol: ""
  resolution: STATIC
  location: MESH_INTERNAL
  endpoints:
  - address: 1.1.1.1
    labels:
      security.istio.io/tlsMode: istio
---
# Set up .Services number of services.

package crypto

import (
	"github.com/minio/minio/internal/config"
	"github.com/minio/pkg/v3/env"
)

const (
	// EnvKMSAutoEncryption is the environment variable used to en/disable
	// SSE-S3 auto-encryption. SSE-S3 auto-encryption, if enabled,
	// requires a valid KMS configuration and turns any non-SSE-C
	// request into an SSE-S3 request.
	// If present EnvAutoEncryption must be either "on" or "off".

apiVersion: release-notes/v2
kind: bug-fix
area: security
releaseNotes:
  - |

- title: Default value of the feature flag `ENABLE_AUTO_SNI` to true
  content: |
    [auto_sni](https://www.envoyproxy.io/docs/envoy/latest/api-v3/config/core/v3/protocol.proto#envoy-v3-api-field-config-core-v3-upstreamhttpprotocoloptions-auto-sni)
    is enabled by default. This means SNI will be set automatically based on the downstream HTTP host/authority header if `DestinationRule` does not explicitly set the same.

name: 'Auto Assign PR to Author'
on:
  pull_request:
    types: [opened]

permissions: {}

jobs:
  add-reviews:
    permissions:
      contents: read  # for kentaro-m/auto-assign-action to fetch config file
      pull-requests: write  # for kentaro-m/auto-assign-action to assign PR reviewers
    runs-on: ubuntu-latest
    steps:

apiVersion: release-notes/v2
kind: bug-fix
area: traffic-management
releaseNotes:
  - |

  auto x = squared_diff_op.getLhs();
  auto y = squared_diff_op.getRhs();
  auto x_type = mlir::dyn_cast<RankedTensorType>(x.getType());
  auto y_type = mlir::dyn_cast<RankedTensorType>(y.getType());
  if (!x_type || !y_type) return failure();
  if (x_type.getShape() != y_type.getShape()) return failure();

  auto result_type = squared_diff_op.getType();

  Scope root = Scope::NewRootScope().ExitOnError();
  auto a = ops::Placeholder(root.WithOpName("A"), DT_FLOAT,
                            ops::Placeholder::Shape({2, 3}));
  auto b = ops::Placeholder(root.WithOpName("B"), DT_FLOAT,
                            ops::Placeholder::Shape({3}));
  auto c = ops::Placeholder(root.WithOpName("C"), DT_FLOAT);
  auto d = ops::Add(root.WithOpName("D"), a, b);
  auto e = ops::Add(root.WithOpName("E"), d, c);

  TensorShape y_shape({2});
  auto y = GatherNd(scope_, x, indices);
  RunTest(x, x_shape, y, y_shape);
}

TEST_F(ArrayGradTest, GatherNdGrad_SliceIndexing) {
  TensorShape shape({2, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto indices = Const(scope_, {{1}, {0}});
  auto y = GatherNd(scope_, x, indices);
  RunTest(x, shape, y, shape);
}

}

template <typename OpTy>
void PrintExecuteOpFuncAttribute(mlir::OpAsmPrinter &p, OpTy op) {
  auto op_func_attrs = op.getOpFuncAttrs();
  if (!op_func_attrs.empty()) {
    auto print_key_value = [&](mlir::Attribute attr) {
      auto key_value = mlir::cast<mlir::ArrayAttr>(attr).getValue();
      auto key = key_value[0];
      auto value = key_value[1];

      p << mlir::cast<mlir::StringAttr>(key).getValue();
      p << " = ";