filesystem_path:
   - tensorflow/c/experimental/filesystem
# security path
security_path:
   - tensorflow/security
# words checklist
segfault_memory:
   - segfault
   - memory leaks
# assignees
filesystem_security_assignee:
   - mihaimaruseac
   
# Cuda Comment
cuda_comment: >
   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:
      * For TF-GPU - See point 1
      * For TF-CPU - See point 2

#   re-running tests. However, note that:
#
#    - New environments like new CUDA versions, changes to manylinux,
#      compilers, etc. can cause undefined behavior such as build failures
#      or tests passing incorrectly.
#    - Automatic LLVM updates are known to extend build time even with
#      the cache; this is unavoidable.
export TFCI=py311,linux_x86,public_cache,disk_cache

# Recommended: Configure Docker. (Linux only)

      status->status = InvalidArgument("Unparseable RunOptions proto");
      return;
    }
    if (run_metadata != nullptr && run_metadata->data != nullptr) {
      status->status =
          InvalidArgument("Passing non-empty run_metadata is invalid.");
      return;
    }

    RunMetadata run_metadata_proto;
    result = session->Run(run_options_proto, input_pairs, output_tensor_names,

`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

// a map (`tensorflow::eager::TensorTape`) from the wrapped tensor to the id of
// the op that produced it (or -1 if this tensor was watched using
// `GradientTape::Watch`.) The op_id is simply a unique index assigned to each
// op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,

  virtual std::vector<tensorflow::Device*> ListAllTfDevices() = 0;

  //===--------------------------------------------------------------------===//
  // Following are helper functions to assist integrating TFRT with current
  // TF eager runtime.
  // TODO(b/172877902): These helper functions are currently used to support
  // PyFuncOp on TFRT, and might be useful for ops that directly use low

///
/// The following data structures incorporate a `void*` that is opaque to
/// TensorFlow but can be used by each filesystem plugin to represent internal
/// data.
///
/// We prefer to have these structures instead of passing `void*` into
/// method signatures to have some type of type safety: for example, operations
/// that are only valid on random access files have a `TF_RandomAccessFile`
/// argument.
///

# kernel registration - REGISTER_OP, REGISTER_KERNEL_BUILDER, etc.
#
# This setting is currently experimental. The 'v2' implementation does _not_
# correspond to a particular, finalized design; rather, it relates to
# developing one.
#
# The current aim of the 'v2' implementation is to allow 'unused' ops and
# kernels to be discarded by the linker (to the benefit of binary size).
bool_flag(

        uses: actions/stale@6f05e4244c9a0b2ed3401882b05d701dd0a7289b # v7.0.0
        with:
          #Comma separated list of labels that can be assigned to issues to exclude them from being marked as stale
          exempt-issue-labels: 'override-stale'
          #Comma separated list of labels that can be assigned to PRs to exclude them from being marked as stale
          exempt-pr-labels: "override-stale"

      stdout=subprocess.PIPE,
      env=maybe_encode_env(environ_cp))

  if proc.wait():
    # Errors from find_cuda_config.py were sent to stderr.
    print('Asking for detailed CUDA configuration...\n')
    return False

  config = dict(
      tuple(line.decode('ascii').rstrip().split(': ')) for line in proc.stdout)

  print('Found CUDA %s in:' % config['cuda_version'])