```Python
old_dict = {
    "old key": "old value",
    "second old key": "second old value",
}
new_dict = {**old_dict, "new key": "new value"}
```

Burada `new_dict`, `old_dict` içindeki tüm key-value çiftlerini ve buna ek olarak yeni key-value çiftini içerir:

```Python
{
    "old key": "old value",
    "second old key": "second old value",

// If the number of dimensions is unknown, `num_dims` must be set to
// -1 and `dims` can be null.  If a dimension is unknown, the
// corresponding entry in the `dims` array must be -1.
TF_CAPI_EXPORT extern void TFE_OpSetAttrShape(TFE_Op* op, const char* attr_name,
                                              const int64_t* dims,
                                              const int num_dims,

// is needed a static_cast can be applied.
class ImmediateExecutionTensorHandle : public AbstractTensorHandle {
 public:
  // Returns number of dimensions.
  virtual absl::Status NumDims(int* num_dims) const = 0;
  // Returns number of elements across all dimensions.
  virtual absl::Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //

```Python
old_dict = {
    "old key": "old value",
    "second old key": "second old value",
}
new_dict = {**old_dict, "new key": "new value"}
```

Ici, `new_dict` contiendra toutes les paires clé-valeur de `old_dict` plus la nouvelle paire clé-valeur :

```Python
{
    "old key": "old value",
    "second old key": "second old value",

TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(
    TFE_Context* ctx, const char* device_name, TF_DataType, const int64_t* dims,
    int num_dims, void* data, size_t len,
    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status);

// Retrieves the address space (i.e. job, replia, task) of the local host and

                                                 TF_Status* s);
void TF_DeleteExecutionContext(TF_ExecutionContext*);

// Represents a (partially-defined) shape.
typedef struct TF_Shape {
  int num_dims;  // Must be >= -1; -1 represents unknown rank.
  int64_t* dim_sizes;
} TF_Shape;

// Add a new parameter to a TensorFlow Function.
TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,

}

TF_Tensor* TFE_AllocateHostTensor(TFE_Context* ctx, TF_DataType dtype,
                                  const int64_t* dims, int num_dims,
                                  TF_Status* status) {
  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  if (ctx == nullptr) {

                                         TF_Status* status) {
  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(),

  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,
      MemoryReleaser memory_releaser, void* memory_releaser_arg) = 0;

  // Create a handle to wrap and manage a Tensor
  virtual ImmediateExecutionTensorHandle* CreateLocalHandle(

  ASSERT_EQ(device_name, nullptr);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  TF_SetStatus(status.get(), TF_OK, "");

  int num_dims = TFE_TensorHandleNumDims(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(num_dims, -1);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  TF_SetStatus(status.get(), TF_OK, "");