"_ = 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.

// Set an AttrValue on the op. Doesn't handle the list types.
void SetOpAttrValueScalar(TFE_Context* ctx, TFE_Op* op,
                          const tensorflow::AttrValue& default_value,
                          const char* attr_name, TF_Status* status);
}  // namespace tensorflow

            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)

	"k8s.io/cli-runtime/pkg/genericiooptions"
	"k8s.io/kubectl/pkg/cmd"
	"k8s.io/kubernetes/cmd/genutils"
)

type cmdOption struct {
	Name         string
	Shorthand    string `yaml:",omitempty"`
	DefaultValue string `yaml:"default_value,omitempty"`
	Usage        string `yaml:",omitempty"`
}

type cmdDoc struct {
	Name             string
	Synopsis         string      `yaml:",omitempty"`

    type: DT_FLOAT
  }
  attr {
    name: "T"
    type: "type"
    description: "Type for images"
    allowed_values {
      list {
        type: DT_UINT8
        type: DT_INT8
      }
    }
    default_value {
      i: 1
    }
  }
  summary: "Summary for op Foo."
  description: "Description for op Foo."
}
)";

void ExpectHasSubstr(StringPiece s, StringPiece expected) {

        arg_attr += '{{tfr.name="{}"'.format(attr_def.name)
        if attr_def.HasField('default_value'):
          default_val = _get_val_from_proto(arg_type, attr_def.default_value)
          arg_attr += ',tfr.default={}'.format(default_val)
        arg_attr += '}'

      idx += 1
      arg_str = '{}: {}{}'.format(arg_name, arg_type, arg_attr)
      arg_list.append(arg_str)