absl::Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));
      status = v2_reader_->Lookup(name, out_tensor->get());
      if (!status.ok()) out_tensor->reset();
    }
  }

  auto dtype = TFE_TensorHandleDataType(t->tensor);
  TFE_CustomDeviceTensorHandleMethods handle_methods;
  handle_methods.num_dims = &LoggedTensorNumDims;
  handle_methods.dim = &LoggedTensorDim;
  handle_methods.deallocator = &LoggedTensorDeallocator;
  return TFE_NewCustomDeviceTensorHandle(context, logging_device_name.c_str(),
                                         dtype, t.release(), handle_methods,

      absl::Span<AbstractTensorHandle* const> output_gradients,
      absl::Span<AbstractTensorHandle*> result) const override;

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  absl::Status BuildOnesLike(const TapeTensor& t,
                             AbstractTensorHandle** result) const override;

  // Looks up the ID of a Gradient.

        the layer's dtype. If you had a model that used `float64`, it will
        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with

  TF_OperationDescription* init(std::string type) {
    // Construct op_name to match the name used by REGISTER_OP in the
    // ATTR_TEST_REGISTER calls above.
    std::string op_name = "CApiAttributesTestOp";
    if (type.find("list(") == 0) {
      op_name += "List";
      type = type.replace(0, 5, "");
      type = type.replace(type.size() - 1, 1, "");
    }
    op_name += type;

#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

// Specification for expected input/output and its type.
// DataType value of DT_INVALID signifies that we don't want to
// check the data type.
typedef std::pair<std::string, DataType> IOSpec;

const char* kFeedStackToString = "File \"feed.cc\", line 10, in alpha";

# Строгая проверка HTTP-заголовка Content-Type { #strict-content-type-checking }

По умолчанию **FastAPI** использует строгую проверку HTTP-заголовка `Content-Type` для JSON-тел запросов. Это означает, что JSON-запросы должны включать корректный заголовок `Content-Type` (например, `application/json`), чтобы тело запроса было обработано как JSON.

## Риск CSRF { #csrf-risk }

# Chequeo estricto de Content-Type { #strict-content-type-checking }

Por defecto, **FastAPI** usa un chequeo estricto del header `Content-Type` para request bodies JSON, esto significa que las requests JSON deben incluir un header `Content-Type` válido (p. ej. `application/json`) para que el request body se parse como JSON.

## Riesgo de CSRF { #csrf-risk }

# 严格的 Content-Type 检查 { #strict-content-type-checking }

默认情况下，FastAPI 对 JSON 请求体使用严格的 `Content-Type` 头检查。这意味着，JSON 请求必须包含有效的 `Content-Type` 头（例如 `application/json`），其请求体才会被按 JSON 解析。

## CSRF 风险 { #csrf-risk }

此默认行为在一个非常特定的场景下，可防御一类跨站请求伪造（CSRF）攻击。

这类攻击利用了浏览器的一个事实：当请求满足以下条件时，浏览器允许脚本在不进行任何 CORS 预检的情况下直接发送请求：

- 没有 `Content-Type` 头（例如使用 `fetch()` 携带 `Blob` 作为 body）
- 且不发送任何认证凭据。

这种攻击主要在以下情况下相关：

- 应用在本地（如 `localhost`）或内网中运行

                            final Map<String, String> indexObj = dataObj.get("index");
                            if (indexObj != null) {
                                if (indexObj.containsKey("_type")) {
                                    indexObj.remove("_type");
                                }
                                final String index = indexObj.get("_index");
                                if (index != null) {