dlm_tensor->dl_tensor.dtype = tf_dlm_type;

  std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;
  std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
  shape_arr->resize(ndim);
  stride_arr->resize(ndim, 1);
  for (int i = 0; i < ndim; i++) {
    (*shape_arr)[i] = tensor->dim_size(i);
  }
  for (int i = ndim - 2; i >= 0; --i) {
    (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];

  outputs_.clear();
  for (TF_Operation* o : outputs) {
    outputs_.emplace_back(TF_Output{o, 0});
  }
  output_values_.resize(outputs_.size());
}

void CSession::SetOutputs(const std::vector<TF_Output>& outputs) {
  ResetOutputValues();
  outputs_ = outputs;
  output_values_.resize(outputs_.size());
}

void CSession::SetTargets(std::initializer_list<TF_Operation*> targets) {
  targets_.clear();

)

if NOT "%FESS_HEAP_SIZE%" == "" (
set FESS_MIN_MEM=%FESS_HEAP_SIZE%
set FESS_MAX_MEM=%FESS_HEAP_SIZE%
)

REM min and max heap sizes should be set to the same value to avoid
REM stop-the-world GC pauses during resize, and so that we can lock the
REM heap in memory on startup to prevent any of it from being swapped
REM out.
set FESS_JAVA_OPTS=%FESS_JAVA_OPTS% -Xms%FESS_MIN_MEM% -Xmx%FESS_MAX_MEM%

   * Asserts that the collection under test contains exactly the given elements, respecting
   * cardinality but not order. Subclasses may override this method to provide stronger assertions,
   * e.g., to check ordering in lists, but realize that <strong>unless a test extends {@link
   * com.google.common.collect.testing.testers.AbstractListTester AbstractListTester}, a call to
   * {@code expectContents()} invokes this version</strong>.
   *

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger

/// check | Verifique

Perceba que você não precisa criar uma classe especial e enviar a dependência para algum outro lugar em que o **FastAPI** a "registre" ou realize qualquer operação similar.

Você apenas envia para `Depends` e o **FastAPI** sabe como fazer o resto.

///

## Compartilhando dependências `Annotated` { #share-annotated-dependencies }

Et dans de nombreux cas, leur lecture serait une opération bloquante (pouvant bloquer la boucle d'événements), car ils sont lus depuis le disque ou le réseau.

/// info

L'exemple ci-dessus est en réalité une exception, car l'objet `io.BytesIO` est déjà en mémoire ; sa lecture ne bloquera donc rien.

Mais dans de nombreux cas, la lecture d'un fichier ou d'un objet de type fichier bloquera.

///

Cela signifie que, même si vos clients d'API ne peuvent envoyer que des chaînes comme clés, tant que ces chaînes contiennent des entiers purs, Pydantic les convertira et les validera.

Et le `dict` que vous recevez dans `weights` aura en réalité des clés `int` et des valeurs `float`.

///

## Récapitulatif { #recap }

/// info

`File` est une classe qui hérite directement de `Form`.

Mais souvenez-vous que lorsque vous importez `Query`, `Path`, `File` et d'autres depuis `fastapi`, ce sont en réalité des fonctions qui renvoient des classes spéciales.

///

/// tip | Astuce