# Tutorial - Pedoman Pengguna - Pengenalan

Tutorial ini menunjukan cara menggunakan ***FastAPI*** dengan semua fitur-fiturnya, tahap demi tahap.

Setiap bagian dibangun secara bertahap dari bagian sebelumnya, tetapi terstruktur untuk memisahkan banyak topik, sehingga kamu bisa secara langsung menuju ke topik spesifik untuk menyelesaikan kebutuhan API tertentu.

Ini juga dibangun untuk digunakan sebagai referensi yang akan datang.

Das kann nützlich sein, wenn Sie beispielsweise in einem Land leben, in dem bestimmte URLs eingeschränkt sind.

### Die automatischen Dokumentationen deaktivieren

Der erste Schritt besteht darin, die automatischen Dokumentationen zu deaktivieren, da diese standardmäßig das Standard-CDN verwenden.

Um diese zu deaktivieren, setzen Sie deren URLs beim Erstellen Ihrer `FastAPI`-App auf `None`:

```Python hl_lines="8"

  %0 = "tf.ReadVariableOp"(%arg0) {_output_shapes = ["tfshape$dim { size: 1 } dim { size: 3 }"], device = "/device:CPU:0", dtype = !tf_type.quint8} : (tensor<!tf_type.resource<tensor<1x3x!tf_type.quint8>>>) -> tensor<1x3x!tf_type.quint8>

  // CHECK: tf.ReadVariableOp
  // CHECK-SAME: dtype = !corert.quint16

  %2 = "tf.MatMul"(%arg0, %1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  // CHECK-NEXT: [[r1:%.*]] = tfrt_fallback_async.executeop {{.*}} "tf.BiasAdd"([[r0]], [[result]])

"tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], Tout = ["tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], _output_shapes = ["tfshape$dim { size: 9 } dim { size: 10 }", "tfshape$dim { size: -1 } dim { size: 9 } dim { size: 10 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$"], _read_only_resource_inputs = [], config = "", config_proto = "\0A\07\0A\03CPU\10\01\0A\07\0A\03GPU\10\002\02J\008\01", device = "", executor_type...

      assert(index[i] < shape[i]);
      assert(index[i] >= 0);
    }
#endif  // NDEBUG
    for (int64_t dim = shape.size() - 1; dim >= 0; --dim) {
      if (dim == fixed_axis) continue;
      ++index[dim];
      if (index[dim] < shape[dim]) return std::move(index);
      index[dim] = 0;
    }
    return std::nullopt;
  }

 protected:

  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
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    value {
      shape {
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    * Autocompletamento.
    * Controllo sulle annotazioni di tipo.
* Validazione dei dati:
    * Errori chiari e automatici quando i dati sono invalidi.
    * Validazione anche per gli oggetti JSON più complessi.
* <abbr title="anche noto come: serializzazione, parsing, marshalling">Conversione</abbr> dei dati di input: da risorse esterne a dati e tipi di Python. È possibile leggere da:
    * JSON.
    * Path parameters.

  llvm::SmallVector<bool, 4> is_reduce_dim(rank, false);
  for (const APInt &index : indices.getValues<APInt>()) {
    int64_t dim = GetDimForAxis(index.getSExtValue(), rank);
    // Invalid input.
    if (dim < 0 || dim >= rank) return UnrankedTensorType::get(element_ty);

    if (!is_reduce_dim[dim]) {
      is_reduce_dim[dim] = true;
      num_reduce_dim++;
    }
  }

  ArrayRef<int64_t> shape = ranked_ty.getShape();

    for (const int64_t dim : batch_dimensions) {
      batch_dimensions_.axes.push_back(dim);
      batch_dimensions_.sizes.push_back(type.getDimSize(dim));
    }

    for (const int64_t dim : contracting_dimensions) {
      contracting_dimensions_.axes.push_back(dim);
      contracting_dimensions_.sizes.push_back(type.getDimSize(dim));
    }

    for (int64_t dim = 0; dim < rank; ++dim) {