)

// Value represents a value of restricted type reduced from an
// expression represented by an ASTNode. Only one of the fields is
// non-nil.
//
// In cases where we are fetching data from a data source (like csv),
// the type may not be determined yet. In these cases, a byte-slice is
// used.
type Value struct {
	value any
}

// Missing is used to indicate a non-existing value.
type Missing struct{}

		return
	}
	switch {
	case target.Type == obj.TYPE_BRANCH:
		// JMP 4(PC)
		*targetAddr = obj.Addr{
			Type:   obj.TYPE_BRANCH,
			Offset: p.pc + 1 + target.Offset, // +1 because p.pc is incremented in append, below.
		}
	case target.Type == obj.TYPE_REG:
		// JMP R1
		*targetAddr = *target
	case target.Type == obj.TYPE_MEM && (target.Name == obj.NAME_EXTERN || target.Name == obj.NAME_STATIC):

                                                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();

        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();

  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;

     * Gets the resource path for configuration files, checking type-specific variants first.
     *
     * @param basePath the base path for resources
     * @param type     the search engine type
     * @param path     the relative path to the resource
     * @return the full resource path
     */
    protected String getResourcePath(final String basePath, final String type, final String path) {

        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

pkg crypto/tls, type Config struct, ServerName string
pkg crypto/tls, type Config struct, Time func() time.Time
pkg crypto/tls, type Conn struct
pkg crypto/tls, type ConnectionState struct
pkg crypto/tls, type ConnectionState struct, CipherSuite uint16
pkg crypto/tls, type ConnectionState struct, HandshakeComplete bool
pkg crypto/tls, type ConnectionState struct, NegotiatedProtocol string

     * <p>
     * If <code>type</code> is not a parameterized type, <code>null</code> is returned.
     * </p>
     *
     * @param type
     *            the type to analyze
     * @return the array of type arguments for the specified type
     * @see ParameterizedType#getActualTypeArguments()
     */
    public static Type[] getGenericParameters(final Type type) {
        if (type instanceof ParameterizedType) {

      "name": {
        "type": "keyword"
      },
      "password": {
        "type": "keyword"
      },
      "surname": {
        "type": "keyword"
      },
      "givenName": {
        "type": "keyword"
      },
      "employeeNumber" : {
        "type" : "keyword"
      },
      "mail" : {
        "type" : "keyword"
      },
      "telephoneNumber" : {
        "type" : "keyword"
      },