TFCI_BAZEL_COMMON_ARGS="--repo_env=HERMETIC_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_gpu_linux"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ARGS="--gpus all"
TFCI_LIB_SUFFIX="-gpu-linux-x86_64"

type promLogger struct{}

func (p promLogger) Println(v ...interface{}) {
	metricsLogIf(GlobalContext, fmt.Errorf("metrics handler error: %v", v))
}

type metricsV3Server struct {
	registry *prometheus.Registry
	opts     promhttp.HandlerOpts
	auth     func(http.Handler) http.Handler

	metricsData *metricsV3Collection
}

var (
	globalMetricsV3CollectorPaths []collectorPath
	globalMetricsV3Once           sync.Once
)

   * failed CAS on base update), the table is initialized to size 2.
   * The table size is doubled upon further contention until
   * reaching the nearest power of two greater than or equal to the
   * number of CPUS. Table slots remain empty (null) until they are
   * needed.
   *
   * A single spinlock ("busy") is used for initializing and
   * resizing the table, as well as populating slots with new Cells.

        "//tensorflow/core/kernels/data:optional_ops_util",
        "//tensorflow/core/platform:abi",
    ]),
    alwayslink = 1,
)

tf_cuda_library(
    name = "ops",
    srcs = [
        "ops.cc",
    ],
    hdrs = [
        "ops.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros_hdrs",
        ":tf_datatype",

  return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0],
                     /*transpose_a=*/false,
                     /*transpose_b=*/false, "MatMul");
}

absl::Status MulModel(AbstractContext* ctx,
                      absl::Span<AbstractTensorHandle* const> inputs,
                      absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Mul(ctx, inputs[0], inputs[1], &outputs[0], "Mul");
}

TFCI_BAZEL_COMMON_ARGS="--repo_env=HERMETIC_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_gpu_linux"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ARGS="--gpus all"
TFCI_LIB_SUFFIX="-gpu-linux-x86_64"

			Type: madmin.SRIAMItemSvcAcc,
			SvcAccChange: &madmin.SRSvcAccChange{
				Update: &madmin.SRSvcAccUpdate{
					AccessKey:     accessKey,
					SecretKey:     opts.secretKey,
					Status:        opts.status,
					Name:          opts.name,
					Description:   opts.description,
					SessionPolicy: updateReq.NewPolicy,
					Expiration:    updateReq.NewExpiration,
				},
			},
			UpdatedAt: updatedAt,
		}))
	}

		return nil, nil, err
	}

	pReader, err := NewPutObjReader(hr).WithEncryption(encHr, &oek)
	if err != nil {
		return nil, nil, err
	}
	opts := &ObjectOptions{
		UserDefined: metadata,
		MTime:       UTCNow(),
		MaxParity:   true,
	}

	return pReader, opts, nil
}

// Reload updates config by reloading remote tier config from config store.

	core := minio.Core{Client: sclnt}
	sobj, _, _, err := core.GetObject(context.Background(), sourceBucket, srcCtnt.Key, opts)
	if err != nil {
		fmt.Printf("unreadable on source: %s (%s)\n", srcCtnt.Key, err)
		return false
	}

	tobj, _, _, err := core.GetObject(context.Background(), targetBucket, tgtCtnt.Key, opts)
	if err != nil {
		fmt.Printf("unreadable on target: %s (%s)\n", tgtCtnt.Key, err)
		return false
	}

	bwRpt := globalNotificationSys.GetBandwidthReports(ctx, bucket)
	bwMap := bwRpt.BucketStats
	for arn, st := range stats.ReplicationStats.Stats {
		for opts, bw := range bwMap {
			if opts.ReplicationARN != "" && opts.ReplicationARN == arn {
				st.BandWidthLimitInBytesPerSecond = bw.LimitInBytesPerSecond
				st.CurrentBandwidthInBytesPerSecond = bw.CurrentBandwidthInBytesPerSecond