// SecretItemDiff represents a secret that has been diffed between nodeagent and proxy
type SecretItemDiff struct {
	Agent string `json:"agent"`
	Proxy string `json:"proxy"`
	SecretItem
}

// SecretItem is an intermediate representation of secrets, used to provide a common
// format between the envoy proxy secrets and node agent output which can be diffed
type SecretItem struct {
	Name        string `json:"resource_name"`

   *
   * <p>This uses an efficient binary recursive algorithm to compute the factorial with balanced
   * multiplies. It also removes all the 2s from the intermediate products (shifting them back in at
   * the end).
   *
   * @throws IllegalArgumentException if {@code n < 0}
   */
  public static BigInteger factorial(int n) {
    checkNonNegative("n", n);

   * enough precision.
   */
  private static final long C1 = 0xcc9e2d51;
  private static final long C2 = 0x1b873593;

  /*
   * This method was rewritten in Java from an intermediate step of the Murmur hash function in
   * http://code.google.com/p/smhasher/source/browse/trunk/MurmurHash3.cpp, which contained the
   * following header:
   *

            // TODO: watch out for module path
        });

        // Note: here, type decides is artifact added to "build path" (for example during resolution)
        // and "build path" is intermediate data that is used to create actual Java classpath/modulepath
        // but to create those, proper filtering should happen via Type properties.
    }

    @Override

   * enough precision.
   */
  private static final long C1 = 0xcc9e2d51;
  private static final long C2 = 0x1b873593;

  /*
   * This method was rewritten in Java from an intermediate step of the Murmur hash function in
   * http://code.google.com/p/smhasher/source/browse/trunk/MurmurHash3.cpp, which contained the
   * following header:
   *

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that
have different security properties and provide different levels of confidence

Como é desencorajado, a documentação interativa com Swagger UI não irá mostrar a documentação para o corpo da requisição para um `GET`, e proxies que intermediarem podem não suportar o corpo da requisição.

///

## Importe o `BaseModel` do Pydantic

Primeiro, você precisa importar `BaseModel` do `pydantic`:

```Python hl_lines="4"

Ceci étant découragé, la documentation interactive générée par Swagger UI ne montrera pas de documentation pour le corps d'une requête `GET`, et les proxys intermédiaires risquent de ne pas le supporter.

///

## Importez le `BaseModel` de Pydantic

Commencez par importer la classe `BaseModel` du module `pydantic` :

{* ../../docs_src/body/tutorial001.py hl[4] *}

         *
         * We carry out this computation in modular arithmetic. Since times2ToThe32Mod accepts any
         * unsigned long, we don't have to do a mod on every operation, only when intermediate
         * results can exceed 2^63.
         */
        long result = times2ToThe32Mod(aHi * bHi /* < 2^62 */, m); // < m < 2^63
        result += aHi * bLo; // aHi * bLo < 2^63, result < 2^64

#-----------------------------------------------------------------------------
# Target: clean
#-----------------------------------------------------------------------------
.PHONY: clean

clean: ## Cleans all the intermediate files and folders previously generated.
	rm -rf $(DIRS_TO_CLEAN)

#-----------------------------------------------------------------------------
# Target: docker