namespace tracing {
namespace graph {

class GraphContext;
class GraphOperation;
class GraphTensor;

auto& kUnknownDim = shape_inference::InferenceContext::kUnknownDim;
auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank;

// GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index
// into the list of outputs for the operation.
class GraphTensor : public TracingTensorHandle {
 public:

#include "tensorflow/core/platform/tstring.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/util/device_name_utils.h"

namespace tensorflow {
class EagerExecutor;
class EagerContext;
class CustomDevice;
class CustomDeviceOpHandler;
class Device;

// LINT.IfChange
// Note: Keep in sync with exported copy of enum in eager/c_api.h.
enum ContextDevicePlacementPolicy {

  if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
    return tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
               unwrapped_handle)
        ->DevicePointer();
  }
  // TODO(b/175427838): It would be nice to be able to use tensorflow::isa here.
  if (!tensorflow::TensorHandle::classof(unwrapped_handle)) {

  for (int i = 0; i < *num_handles; ++i) {
    tensorflow::ImmediateExecutionTensorHandle* unwrapped_handle =
        tensorflow::unwrap(handles[i]);
    if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =

```Python
something(some_argument, some_keyword_argument="foo")
```

в таком случае он является «вызываемым».

## Классы как зависимости { #classes-as-dependencies_1 }

Вы можете заметить, что для создания экземпляра класса в Python используется тот же синтаксис.

Например:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

          return classify(Integer.compare(left, right));
        }

        @Override
        public ComparisonChain compare(long left, long right) {
          return classify(Long.compare(left, right));
        }

        @Override
        public ComparisonChain compare(float left, float right) {
          return classify(Float.compare(left, right));
        }

   * all super-classes, that are annotated with {@code @Subscribe}. The cache is shared across all
   * instances of this class; this greatly improves performance if multiple EventBus instances are
   * created and objects of the same class are registered on all of them.
   */
  private static final LoadingCache<Class<?>, ImmutableList<Method>> subscriberMethodsCache =

Concrete classes Cycles Name Number of classes Best Fragment Size Girth Radius Diameter Layer Package Cycles Name Number of packagess Best Fragment Size Girth Radius Diameter Layer Layers Layer Number of classes Classes of layer : Classes and Packages Click on or to go to the cycle to which the class/package belongs. Class/Package Size Used by Uses internal Uses external Layer Source(s) # Member of cycle Classes using : uses: true uses: Packages using : uses: true uses: Classes of cycle : Best fragmenter(s)...

La classe `Hero` est très similaire à un modèle Pydantic (en fait, en dessous, c'est réellement un modèle Pydantic).

Il y a quelques différences :

* `table=True` indique à SQLModel qu'il s'agit d'un *modèle de table*, il doit représenter une **table** dans la base SQL, ce n'est pas seulement un *modèle de données* (comme le serait n'importe quelle autre classe Pydantic classique).

  @VisibleForTesting
  List<Class<?>> findClassesToTest(
      Iterable<? extends Class<?>> classes, Iterable<String> explicitTestNames) {
    // "a.b.Foo" -> a.b.Foo.class
    TreeMap<String, Class<?>> classMap = Maps.newTreeMap();
    for (Class<?> cls : classes) {
      classMap.put(cls.getName(), cls);
    }
    // Foo.class -> [FooTest.class, FooTests.class, FooTestSuite.class, ...]