# Where component owners are known, add them here.

/tensorflow/c/eager @qqfish
/tensorflow/core/common_runtime/eager @qqfish
/tenosrflow/core/debug @caisq
/tensorflow/core/kernels/mkl/ @penpornk
/tensorflow/core/kernels/sparse/ @penpornk
/tensorflow/core/nccl/ @azaks2 @chsigg
/tensorflow/python/autograph/ @mdanatg
/tensorflow/python/debug @caisq
/tensorflow/python/eager @rohan100jain
/tensorflow/tools/docs/ @markdaoust

    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

// Dynamic cluster API.

// Update an existing context with a new set of servers defined in a ServerDef
// proto. Servers can be added to and removed from the list of remote workers
// in the context. A New set of servers identified by the ServerDef must be up
// when the context is updated.
//
// This API is for experimental usage and may be subject to change.

*   `tf.data`:

    *   Added `target_workers` param to `data_service_ops.from_dataset_id` and
        `data_service_ops.distribute`. Users can specify `"AUTO"`, `"ANY"`, or
        `"LOCAL"` (case insensitive). If `"AUTO"`, tf.data service runtime
        decides which workers to read from. If `"ANY"`, TF workers read from any
        tf.data service workers. If `"LOCAL"`, TF workers will only read from

vulnerabilities to the vendor of the affected hardware accelerator.

## Reporting vulnerabilities

### Vulnerabilities in TensorFlow

This document covers different use cases for TensorFlow together with comments
whether these uses were recommended or considered safe, or where we recommend
some form of isolation when dealing with untrusted data. As a result, this

///     break ABI compatibility and cause all plugins to be recompiled, these
///     type of changes should be extremely rare.
///
/// Next section will detail this as well as some corner cases that are out of
/// scope for now.

// LINT.IfChange
typedef struct TF_RandomAccessFileOps {
  /// Releases resources associated with `*file`.
  ///

                                      bool clear_existing_contexts = false) = 0;

  // Initializes context for the local worker and no contexts will be created
  // for remote workers. Currently this only works for resetting context.
  // TODO(b/289445025): Consider removing this when we find a proper fix.
  virtual Status InitializeLocalOnlyContext(const ServerDef& server_def,

  }

  return single_host_server_def;
}

tensorflow::ServerDef GetClusterServerDef(const string& worker_job_name,
                                          int num_workers) {
  tensorflow::ServerDef server_def = GetServerDef(worker_job_name, num_workers);
  tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster();

  // Add a client.
  tensorflow::JobDef* job_def2 = cluster_def->add_job();

  FileSystem* fs = nullptr;
  Status s = env_->GetFileSystemForFile(generic_path, &fs);
  if (fs == nullptr || !s.ok())
    GTEST_SKIP() << "No filesystem registered: " << s;

  // First, test some interesting corner cases concerning empty URIs
  if (GetParam().empty()) {
    EXPECT_EQ(fs->TranslateName(""), "");
    EXPECT_EQ(fs->TranslateName("/"), "/");
    EXPECT_EQ(fs->TranslateName("//"), "/");

TF_CAPI_EXPORT extern TFE_ContextDevicePlacementPolicy
TFE_ContextGetDevicePlacementPolicy(TFE_Context* ctx);

// A tensorflow.ServerDef specifies remote workers (in addition to the current
// workers name). Operations created in this context can then be executed on
// any of these remote workers by setting an appropriate device.
//
// If the following is set, all servers identified by the
// ServerDef must be up when the context is created.