}

TFE_TensorHandle* TFE_NewTensorHandleFromScalar(TF_DataType data_type,
                                                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({}));

    for (const auto& p : *shapes_and_types) {
      auto* out_shape_and_type = handle_data.add_shape_and_type();
      ic->ShapeHandleToProto(p.shape, out_shape_and_type->mutable_shape());
      out_shape_and_type->set_dtype(p.dtype);
      *out_shape_and_type->mutable_type() = p.type;
    }
  }
  string str_data;
  handle_data.SerializeToString(&str_data);

  }

  tensorflow::AbstractTensorInterface* t =
      tensorflow::unwrap(ctx)->CreateTensor(
          static_cast<tensorflow::DataType>(dtype), dimvec);

  if (t == nullptr) {
    status->status =
        tensorflow::errors::InvalidArgument("Unsupported dtype: ", dtype);
    return nullptr;
  }

  return new TF_Tensor{t};
}

TFE_TensorHandle* TFE_NewTensorHandleFromTensor(TFE_Context* ctx, TF_Tensor* t,

        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

  TF_OperationDescription* init(string type) {
    // Construct op_name to match the name used by REGISTER_OP in the
    // ATTR_TEST_REGISTER calls above.
    string op_name = "CApiAttributesTestOp";
    if (type.find("list(") == 0) {
      op_name += "List";
      type = type.replace(0, 5, "");
      type = type.replace(type.size() - 1, 1, "");
    }
    op_name += type;
    return TF_NewOperation(

}

// getSigningKey hmac seed to calculate final signature.
func getSigningKey(secretKey string, t time.Time, region string, stype serviceType) []byte {
	date := sumHMAC([]byte("AWS4"+secretKey), []byte(t.Format(yyyymmdd)))
	regionBytes := sumHMAC(date, []byte(region))
	service := sumHMAC(regionBytes, []byte(stype))
	signingKey := sumHMAC(service, []byte("aws4_request"))
	return signingKey
}

// 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 ||

				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{

        CURRENT = JAVA6;
      }
    }

    abstract Type newArrayType(Type componentType);

    abstract Type usedInGenericType(Type type);

    final ImmutableList<Type> usedInGenericType(Type[] types) {
      ImmutableList.Builder<Type> builder = ImmutableList.builder();
      for (Type type : types) {
        builder.add(usedInGenericType(type));
      }
      return builder.build();
    }

                            "type": "array",
                            "items": {
                                "anyOf": [{"type": "string"}, {"type": "integer"}]
                            },
                        },
                        "msg": {"title": "Message", "type": "string"},
                        "type": {"title": "Error Type", "type": "string"},
                    },
                },