TEST(CAPI, TestTensorNonScalarBytesAllocateDelete) {
  const int batch_size = 4;
  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }
  TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims,
                                   sizeof(TF_TString) * num_elements);

      } else {
        const auto num_dims = tensor_shape.dim_size();
        std::unique_ptr<int64_t[]> dims(new int64_t[num_dims]);
        for (int i = 0; i < num_dims; ++i) {
          dims[i] = tensor_shape.dim(i).size();
        }
        TFE_OpSetAttrShape(op, attr_name, dims.get(), num_dims, status);
      }
    } break;
    case tensorflow::AttrValue::kFunc: {

absl::Status SetAttrShape(AbstractOperation* op_, const char* attr_name,
                          const int64_t* dims, const int num_dims,
                          ForwardOperation* forward_op_) {
  if (num_dims > TensorShape::MaxDimensions()) {
    return absl::InvalidArgumentError(
        absl::StrCat("Value specified for `", attr_name, "` has ", num_dims,
                     " dimensions which is over the limit of ",

    CHECK_EQ(output_shapes->num_items, 1);

    int num_dims = output_shapes->items[0].num_dims;
    int64_t* dims = output_shapes->items[0].dims;

    if (!expected_shape.has_value()) {
      EXPECT_EQ(num_dims, -1);
      EXPECT_EQ(dims, nullptr);
      return;
    }

    EXPECT_EQ(num_dims, expected_shape->size());
    for (size_t i = 0; i < num_dims; ++i) {
      EXPECT_EQ(dims[i], (*expected_shape)[i]);
    }

そのような場合、Python の `**dict_to_unpack` による `dict` の「アンパック」テクニックを使えます:

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

ここでは、`new_dict` には `old_dict` のすべてのキーと値に加え、新しいキーと値が含まれます:

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

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

Тут `new_dict` міститиме всі пари ключ-значення з `old_dict` плюс нову пару ключ-значення:

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

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

Здесь `new_dict` будет содержать все пары ключ-значение из `old_dict` плюс новую пару ключ-значение:

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

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

Aqui, o `new_dict` terá todos os pares de chave-valor do `old_dict` mais o novo par de chave-valor:

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

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

Here, `new_dict` will contain all the key-value pairs from `old_dict` plus the new key-value pair:

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

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

Aquí, `new_dict` contendrá todos los pares clave-valor de `old_dict` más el nuevo par clave-valor:

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