namespace tensorflow {
void SetOpAttrValueScalar(TFE_Context* ctx, TFE_Op* op,
                          const tensorflow::AttrValue& default_value,
                          const char* attr_name, TF_Status* status) {
  switch (default_value.value_case()) {
    case tensorflow::AttrValue::kS: {
      const string& v = default_value.s();
      TFE_OpSetAttrString(op, attr_name, v.data(), v.size());
      break;
    }

}

string AttrView::InputArg(bool with_default_value) const {
  string default_value = DefaultValue();
  if (!with_default_value || default_value.empty()) {
    return absl::Substitute("$0 $1", VariableType(), attr_.name());
  }
  return absl::Substitute("$0 $1 = $2", VariableType(), attr_.name(),
                          default_value);
}

string AttrView::SetterMethod() const {
  if (!attr_.is_list()) {

              filter_enabled:
                runtime_key: test_enabled
                default_value:
                  numerator: 100
                  denominator: HUNDRED
              filter_enforced:
                runtime_key: test_enabled
                default_value:
                  numerator: 100
                  denominator: HUNDRED
              response_headers_to_add:

// `default_value`.
template <typename T>
static inline std::vector<T> GetOptionalVector(
    std::optional<DenseElementsAttr> elements, int64_t default_size = 0,
    int64_t default_value = 0) {
  if (elements.has_value()) {
    return GetVector<T>(elements.value());
  }
  return std::vector<T>(default_size, default_value);
}

                       "_ = x;\n");
    strings::StrAppend(&setters, "      return ret;\n    }\n\n");

    string field_initiliazer;
    auto& default_value = api_def_attr.default_value();
    if (default_value.value_case() == AttrValue::kList &&
        !IsEmptyList(default_value.list())) {
      // Non-empty lists need static storage for their defaults. Define a
      // function with static local variable that stores the array.

            field_info = params.File(annotation=use_annotation, default=default_value)
        elif not field_annotation_is_scalar(annotation=type_annotation):
            field_info = params.Body(annotation=use_annotation, default=default_value)
        else:
            field_info = params.Query(annotation=use_annotation, default=default_value)

    field = None
    if field_info is not None:
        if is_path_param:

   * DEFAULT_VALUE} if this method call takes over 50 ms, you can use this code:
   *
   * <pre>
   *   TimeLimiter limiter = . . .;
   *   TargetType proxy = limiter.newProxy(
   *       target, TargetType.class, 50, TimeUnit.MILLISECONDS);
   *   try {
   *     return proxy.someMethod();
   *   } catch (UncheckedTimeoutException e) {
   *     return DEFAULT_VALUE;
   *   }
   * </pre>
   *

              OpDef_AttrDef attr =
                  *FindAttr(arg.type_attr(), op_info.graph_op_def);
              if (attr.has_default_value() &&
                  attr.default_value().value_case() == AttrValue::kType) {
                type = DataType_Name(attr.default_value().type());
              } else if (attr.has_allowed_values() &&
                         attr.allowed_values().value_case() ==
                             AttrValue::kList &&

   * DEFAULT_VALUE} if this method call takes over 50 ms, you can use this code:
   *
   * <pre>
   *   TimeLimiter limiter = . . .;
   *   TargetType proxy = limiter.newProxy(
   *       target, TargetType.class, 50, TimeUnit.MILLISECONDS);
   *   try {
   *     return proxy.someMethod();
   *   } catch (UncheckedTimeoutException e) {
   *     return DEFAULT_VALUE;
   *   }
   * </pre>
   *

      keys=[1, 2, 3, 4],
      values=[5, 6, 7, 8],
      key_dtype=tf.int32,
      value_dtype=tf.float32)
  table = tf.lookup.StaticHashTable(
      table_initializer, default_value=tf.constant(0.0))

  x = tf.placeholder(tf.int32, shape=(), name='input')
  y = table.lookup(x)
  r = tf.add(y, z)

  tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)