void* data, size_t len,
                                                TF_Status* status) {
  auto dtype = static_cast<tensorflow::DataType>(data_type);
  DCHECK(tensorflow::DataTypeCanUseMemcpy(dtype));

  tensorflow::Tensor tensor(dtype, tensorflow::TensorShape({}));
  std::memcpy(tensorflow::TensorCApi::Buffer(tensor)->data(), data, len);

  status->status = absl::OkStatus();

        the layer's dtype. If you had a model that used `float64`, it will
        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with

}

func reqSignatureV4Verify(r *http.Request, region string, stype serviceType) (s3Error APIErrorCode) {
	sha256sum := getContentSha256Cksum(r, stype)
	switch {
	case isRequestSignatureV4(r):
		return doesSignatureMatch(sha256sum, r, region, stype)
	case isRequestPresignedSignatureV4(r):
		return doesPresignedSignatureMatch(sha256sum, r, region, stype)
	default:
		return ErrAccessDenied
	}
}

	}
}

// metricDisplay - contains info on a metric for display purposes.
type metricDisplay struct {
	Name   string   `json:"name"`
	Help   string   `json:"help"`
	Type   string   `json:"type"`
	Labels []string `json:"labels"`
}

func (md metricDisplay) String() string {
	return fmt.Sprintf("Name: %s\nType: %s\nHelp: %s\nLabels: {%s}\n", md.Name, md.Type, md.Help, strings.Join(md.Labels, ","))
}

				MetricV2{
					Description: MetricDescription{
						Namespace: bucketMetricNamespace,
						Subsystem: "batch",
						Name:      MetricName(jtype + "_objects"),
						Help:      "Get successfully completed batch job " + jtype + "objects",
						Type:      counterMetric,
					},
					Value:          objects,
					VariableLabels: map[string]string{"bucket": bucket, "jobId": mj.JobID},
				},
				MetricV2{

// guessIsMetricsReq - returns true if incoming request looks
// like metrics request
func guessIsMetricsReq(req *http.Request) bool {
	if req == nil {
		return false
	}
	aType := getRequestAuthType(req)
	return (aType == authTypeAnonymous || aType == authTypeJWT) &&
		req.URL.Path == minioReservedBucketPath+prometheusMetricsPathLegacy ||
		req.URL.Path == minioReservedBucketPath+prometheusMetricsV2ClusterPath ||

        firstLineInnerMap.put("_index", index);
        firstLineInnerMap.put("_type", type);
        firstLineInnerMap.put("_id", item.getId());
        firstLineMap.put("index", firstLineInnerMap);

        final Map<String, Object> secondLine = new HashMap<>();

        secondLine.put("text", item.getText());

        // reading
        final String[][] readings = item.getReadings();

        throw new IllegalArgumentException("No type mapping from " + fromClass + " to " + to);
      }
    }.visit(from);
  }

  /**
   * Resolves all type variables in {@code type} and all downstream types and returns a
   * corresponding type with type variables resolved.
   */
  public Type resolveType(Type type) {
    checkNotNull(type);
    if (type instanceof TypeVariable) {

     * Create new RDMA work request
     *
     * @param requestId unique request identifier
     * @param type type of request
     * @param memoryRegion associated memory region
     */
    public RdmaWorkRequest(long requestId, RequestType type, RdmaMemoryRegion memoryRegion) {
        this.requestId = requestId;
        this.type = type;
        this.memoryRegion = memoryRegion;
        this.completed = false;
    }

    /**

	}{
		{
			name:     "1",
			metadata: map[string]string{"content-type": "application/octet-stream", "etag": "de75a98baf2c6aef435b57dd0fc33c86", "x-amz-storage-class": "STANDARD"},
			want:     map[string]string{"content-type": "application/octet-stream", "etag": "de75a98baf2c6aef435b57dd0fc33c86"},
		},
		{
			name:     "2",