TF_DeleteImportGraphDefOptions(opts);
  opts = TF_NewImportGraphDefOptions();
  TF_ImportGraphDefOptionsSetPrefix(opts, "imported2");
  TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
  TF_ImportGraphDefOptionsAddReturnOutput(opts, "feed", 0);
  TF_ImportGraphDefOptionsAddReturnOutput(opts, "scalar", 0);
  EXPECT_EQ(2, TF_ImportGraphDefOptionsNumReturnOutputs(opts));

func Open(dialector Dialector, opts ...Option) (db *DB, err error) {
	config := &Config{}

	sort.Slice(opts, func(i, j int) bool {
		_, isConfig := opts[i].(*Config)
		_, isConfig2 := opts[j].(*Config)
		return isConfig && !isConfig2
	})

	if len(opts) > 0 {
		if c, ok := opts[0].(*Config); ok {
			config = c
		} else {
			opts = append([]Option{config}, opts...)
		}
	}

// and is re-computed by the runtime.
inline void TF_ImportGraphDefOptionsSetPropagateDeviceSpec(
    TF_ImportGraphDefOptions* opts, unsigned char propagate_device_spec) {
  opts->opts.propagate_device_spec = propagate_device_spec;
}

}  // end namespace tensorflow

      ASSERT_EQ(expected[i], data[i]) << "Different data at index " << i;
    }
  }

  std::vector<TF_Output> ToOutput(const std::vector<TF_Operation*> ops) {
    std::vector<TF_Output> out;
    for (auto op : ops) {
      out.push_back({op, 0});
    }
    return out;
  }

  void Define(int num_opers, const std::vector<TF_Operation*>& opers,

# A shared object which includes registration mechanisms for ops and
# kernels. Does not include the implementations of any ops or kernels. Instead,
# the library which loads libtensorflow_framework.so
# (e.g. _pywrap_tensorflow_internal.so for Python, libtensorflow.so for the C
# API) is responsible for registering ops with libtensorflow_framework.so. In
# addition to this core set of ops, user libraries which are loaded (via

# nothing in a bazelrc will default to a monolithic build. The following line
# opts in to modular op registration support by default.
common --define framework_shared_object=true
common --define tsl_protobuf_header_only=true

common --define=allow_oversize_protos=true

common --spawn_strategy=standalone
common -c opt

common --repo_env=USE_PYWRAP_RULES=True
common --copt=-DGRPC_BAZEL_BUILD

        "//tensorflow/core/platform:abi",
    ]),
    alwayslink = 1,
)

tf_cuda_library(
    name = "ops",
    srcs = [
        "ops.cc",
    ],
    hdrs = [
        "ops.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros_hdrs",
        ":tf_datatype",
        ":tf_status_helper",

- kube-apiserver disables the beta WatchList feature by default in 1.33 in favor of the `StreamingCollectionEncodingToJSON` and `StreamingCollectionEncodingToProtobuf` features.kube-controller-manager no longer opts into enabling the WatchListClient feature in 1.33. ([#131359](https://github.com/kubernetes/kubernetes/pull/131359), [@deads2k](https://github.com/deads2k)) [SIG API Machinery]

## Dependencies

### Added

//// tab | Linux, macOS

Let's say you install Python and it ends up in a directory `/opt/custompython/bin`.

If you say yes to update the `PATH` environment variable, then the installer will add `/opt/custompython/bin` to the `PATH` environment variable.

It could look like this:

```plaintext
/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/opt/custompython/bin
```

Cela reviendrait à peu près à taper :

<div class="termy">

```console
$ /opt/custompython/bin/python
```

</div>

////

//// tab | Windows

Le système va **trouver** le programme `python` dans `C:\opt\custompython\bin\python` et l'exécuter.

Cela reviendrait à peu près à taper :

<div class="termy">

```console