### New unique package

The new [unique](/pkg/unique) package provides facilities for
canonicalizing values (like "interning" or "hash-consing").

Any value of comparable type may be canonicalized with the new
`Make[T]` function, which produces a reference to a canonical copy of
the value in the form of a `Handle[T]`.
Two `Handle[T]` are equal if and only if the values used to produce the
handles are equal, allowing programs to deduplicate values and reduce

		FieldF2 string `gorm:"uniqueIndex:uniq_field_f1_f2;"`

		FieldG string `gorm:"unique;uniqueIndex"` // unique and uniqueIndex

		FieldH1 string `gorm:"unique;uniqueIndex:uniq_field_h1_h2"` // unique and mul uniqueIndex
		FieldH2 string `gorm:"uniqueIndex:uniq_field_h1_h2"`        // unique and mul uniqueIndex
	}
	indexSchema, err := schema.Parse(&IndexTest{}, &sync.Map{}, schema.NamingStrategy{})

	} ShareCtr;

	[op(0x0f)]
	int ShareEnumAll([in,string,unique] wchar_t *servername,
			[in,out] int *level,
			[in,out,switch_is(*level)] ShareCtr *info,
			[in] unsigned long prefmaxlen,
			[out] unsigned long *totalentries,
			[in,out] unsigned long *resume_handle);

	[op(0x10)]
	int ShareGetInfo([in,string,unique] wchar_t *servername,
			[in,string] wchar_t *sharename,
			[in] int level,

pkg unique, func Make[$0 comparable]($0) Handle[$0] #62483
pkg unique, method (Handle[$0]) Value() $0 #62483

namespace tensorflow {
namespace parallel_device {

TEST(PARALLEL_DEVICE, TestRemoteBasic) {
  std::unique_ptr<TFE_ContextOptions, decltype(&TFE_DeleteContextOptions)> opts(
      TFE_NewContextOptions(), TFE_DeleteContextOptions);
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  std::unique_ptr<TFE_Context, decltype(&TFE_DeleteContext)> context(

			Name:  "uni_user_uniques_name1",
			Field: &schema.Field{Name: "Name1", Unique: true},
		},
	}
	for k, result := range results {
		v, ok := constraints[k]
		if !ok {
			t.Errorf("Failed to found unique constraint %v from parsed constraints %+v", k, constraints)
		}
		tests.AssertObjEqual(t, result, v, "Name")
		tests.AssertObjEqual(t, result.Field, v.Field, "Name", "Unique", "UniqueIndex")
	}

	Nullable() (nullable bool, ok bool)
	Unique() (unique bool, ok bool)
	ScanType() reflect.Type
	Comment() (value string, ok bool)
	DefaultValue() (value string, ok bool)
}

type Index interface {
	Table() string
	Name() string
	Columns() []string
	PrimaryKey() (isPrimaryKey bool, ok bool)
	Unique() (unique bool, ok bool)
	Option() string
}

// TableType table type interface

  BuildV2VarMaps();

  // Invariant: exactly one of "reader_" and "v2_reader_" is non-null.
  std::unique_ptr<TensorSliceReader> reader_;
  std::unique_ptr<BundleReader> v2_reader_;

  std::unique_ptr<TensorSliceReader::VarToShapeMap> var_to_shape_map_;
  std::unique_ptr<TensorSliceReader::VarToDataTypeMap> var_to_data_type_map_;

  CheckpointReader(const CheckpointReader&) = delete;

  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};

struct TFE_MonitoringBuckets {
  explicit TFE_MonitoringBuckets(
      std::function<std::unique_ptr<tensorflow::monitoring::Buckets>(void)>
          fn) {
    create_buckets = fn;
  }

  std::function<std::unique_ptr<tensorflow::monitoring::Buckets>(void)>
      create_buckets;
};

struct TFE_MonitoringSamplerCell {
  tensorflow::monitoring::SamplerCell cell;

from fastapi import FastAPI
from fastapi.routing import APIRoute
from pydantic import BaseModel


def custom_generate_unique_id(route: APIRoute):
    return f"{route.tags[0]}-{route.name}"


app = FastAPI(generate_unique_id_function=custom_generate_unique_id)


class Item(BaseModel):
    name: str
    price: float


class ResponseMessage(BaseModel):
    message: str


class User(BaseModel):
    username: str