if (_os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "true" or
    _os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "1"):
  import tensorflow_io_gcs_filesystem as _tensorflow_io_gcs_filesystem

# Lazy-load Keras v2/3.
_tf_uses_legacy_keras = (
    _os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"))
setattr(_current_module, "keras", _KerasLazyLoader(globals()))

	report()
	fatalErr(err)

	// At this point the version stack must look as below,
	// v3 --> free version      00000000-0000-0000-0000-0000000000f2 (from removal of null version)
	// v2 --> free version      00000000-0000-0000-0000-0000000000f1 (from overwriting of null version )
	// v1 --> non-free version  00000000-0000-0000-0000-000000000001

	// Check number of free-versions

    return e1().getKey();
  }

  protected final V v1() {
    return e1().getValue();
  }

  protected final K k2() {
    return e2().getKey();
  }

  protected final V v2() {
    return e2().getValue();
  }

  protected final K k3() {
    return e3().getKey();
  }

  protected final V v3() {
    return e3().getValue();
  }

  protected final K k4() {

and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.
Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

	// Renames and schedules for purging all bucket metacache.
	go renameAllBucketMetacache(diskPath)
}

// Following error message is added to fix a regression in release
// RELEASE.2018-03-16T22-52-12Z after migrating v1 to v2 to v3. This
// migration failed to capture '.This' field properly which indicates
// the disk UUID association. Below error message is returned when
// we see this situation in format.json, for more info refer

# Hook external TensorFlow modules.
# Import compat before trying to import summary from tensorboard, so that
# reexport_tf_summary can get compat from sys.modules. Only needed if using
# lazy loading.
_current_module.compat.v2  # pylint: disable=pointless-statement

# Load tensorflow-io-gcs-filesystem if enabled
if (_os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "true" or
    _os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "1"):

    return e1().getKey();
  }

  protected final V v1() {
    return e1().getValue();
  }

  protected final K k2() {
    return e2().getKey();
  }

  protected final V v2() {
    return e2().getValue();
  }

  protected final K k3() {
    return e3().getKey();
  }

  protected final V v3() {
    return e3().getValue();
  }

  protected final K k4() {

            .expectedValuesPerKey(2)
            .orderValuesBy(Ordering.natural());
    HashHostileComparable v1 = new HashHostileComparable("value1");
    HashHostileComparable v2 = new HashHostileComparable("value2");
    builder.put("key", v1);
    builder.put("key", v2);
    assertThat(builder.build().entries()).hasSize(2);
  }

  public void testBuilder_withImmutableEntry() {
    ImmutableSetMultimap<String, Integer> multimap =

    "price": 50.2
}
```

/// info

In Pydantic v1 hieß diese Methode `.dict()`, in Pydantic v2 wurde sie deprecated (aber immer noch unterstützt) und in `.model_dump()` umbenannt.

Die Beispiele hier verwenden `.dict()` für die Kompatibilität mit Pydantic v1, Sie sollten jedoch stattdessen `.model_dump()` verwenden, wenn Sie Pydantic v2 verwenden können.

///

/// info

### Replicating Deletes