Puedes utilizar esta plantilla para comenzar, ya que incluye gran parte de la configuración inicial, seguridad, base de datos y algunos endpoints de API ya realizados.

Repositorio en GitHub: [Full Stack FastAPI Template](https://github.com/tiangolo/full-stack-fastapi-template)

* **Rápido de programar**: Incrementa la velocidad de desarrollo entre 200% y 300%. *
* **Menos errores**: Reduce los errores humanos (de programador) aproximadamente un 40%. *
* **Intuitivo**: Gran soporte en los editores con <abbr title="conocido en inglés como auto-complete, autocompletion, IntelliSense, completion">auto completado</abbr> en todas partes. Gasta menos tiempo <abbr title="buscando y corrigiendo errores">debugging</abbr>.

Nếu bạn có thể chọn một phiên bản Python gần đây hơn cho dự án của bạn, ban sẽ có được những ưu điểm của những cải tiến đơn giản đó.

Trong tất cả các tài liệu tồn tại những ví dụ tương thích với mỗi phiên bản Python (khi có một sự khác nhau).

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#343a40}.card-gray-dark.card-outline-tabs>.card-header a:hover{border-top:3px solid #dee2e6}.card-gray-dark.card-outline-tabs>.card-header a.active,.card-gray-dark.card-outline-tabs>.card-header a.active:hover{border-top:3px solid #343a40}.bg-gradient-gray-dark>.card-header .btn-tool,.bg-gray-dark>.card-header .btn-tool,.card-gray-dark:not(.card-outline)>.card-header .btn-tool{color:rgba(255,255,255,.8)}.bg-gradient-gray-dark>.card-header .btn-tool:hover,.bg-gray-dark>.card-header .btn-tool:hove...

  if (TF_GetCode(status) != TF_OK) return;
  if (!grad) return;

  status->status = g->graph.AddFunctionDef(grad->record->fdef(),
                                           grad->record->stack_traces());
  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());

  for (int i{}; i < inputs.size(); ++i) {
    theta_inputs[i] = inputs[i];
  }

  AbstractTensorHandle* theta =
      theta_inputs[input_index];  // parameter we are grad checking

  // Convert from AbstractTensor to TF_Tensor.
  TF_Tensor* theta_tensor;
  TF_RETURN_IF_ERROR(GetValue(theta, &theta_tensor));

  // Get number of elements and fill data.

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	//
	i, locksFailed := 0, 0
	done := false

	for ; i < len(restClnts); i++ { // Loop until we acquired all locks
		select {
		case grant := <-ch:
			if grant.isLocked() {
				// Mark that this node has acquired the lock
				(*locks)[grant.index] = grant.lockUID
			} else {
				locksFailed++
				if locksFailed > tolerance {
					// We know that we are not going to get the lock anymore,

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/unified_api_testutil.h"

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *