// First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);
  TF_Status* status = TF_NewStatus();
  TF_Operation* arg = TF_FinishOperation(arg_descr, status);
  ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
  TF_OperationDescription* id_descr =

  // Summarizes the value of this tensor. The caller takes ownership of the
  // returned buffer. If `status` is not TF_OK, instead returns a null pointer.
  //
  // Does not include the shape and dtype of the tensor (which is generally
  // appended later), but should include any information specific to this custom
  // device which would be useful for debugging.
  //

            metadata.type = TF_ATTR_INT;
          } else if (typestr == "list(float)") {
            metadata.type = TF_ATTR_FLOAT;
          } else if (typestr == "list(bool)") {
            metadata.type = TF_ATTR_BOOL;
          } else if (typestr == "list(type)") {
            metadata.type = TF_ATTR_TYPE;
          } else if (typestr == "list(shape)") {
            metadata.type = TF_ATTR_SHAPE;

  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, "ReadVariableOp", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpAddInput(op, var, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  int num_retvals = 1;
  TFE_Execute(op, out_value, &num_retvals, status);

        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";

     *            The string. Must not be {@literal null}.
     * @param ctype
     *            The array of character types. Must not be {@literal null} or empty.
     */
    public Tokenizer(final String str, final byte[] ctype) {
        assertArgumentNotNull("str", str);
        assertArgumentNotEmpty("ctype", ctype);

        this.str = str;
        this.ctype = ctype;
    }

    /**
     * Sets up the character type array.

     * Artifact type name for a JAR file containing test sources.
     */
    String TEST_JAVA_SOURCE = "test-java-source";

    /**
     * Returns the dependency type id.
     * The id uniquely identifies this <i>dependency type</i>.
     *
     * @return the id of this type, never {@code null}.
     */
    @Nonnull
    @Override
    String id();

    /**
     * Returns the dependency type language.
     *

# 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 }