///
/// 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.
///

  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).

  else:
    p.text += (
        "\n      Since there were not three failures, this is probably a flake."
    )
    p.text += (
        "\n      Flakes make this pkg/pip_and_nonpip_tests target show "
        "as failing,"
    )
    p.text += "\n      but do not make the Kokoro invocation fail."

os.makedirs(os.path.dirname(sys.argv[2]), exist_ok=True)
result.update_statistics()

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## Install

See the [TensorFlow install guide](https://www.tensorflow.org/install) for the
[pip package](https://www.tensorflow.org/install/pip), to

  // If the `shape` argument to `FromTensorHandles` is specified, returns that.
  //
  // Otherwise if all of the tensors have the same shape, returns that via the
  // `shape` output argument. This blocks waiting for async tensors, may return
  // a delayed bad status encountered during async execution, and will return a
  // bad status unless all tensors have the same shape.
  Status Shape(const std::vector<int64_t>** shape) const;

// --------------------------------------------------------------------------
// In-process TensorFlow server functionality, for use in distributed training.
// A Server instance encapsulates a set of devices and a Session target that
// can participate in distributed training. A server belongs to a cluster
// (specified by a ClusterSpec), and corresponds to a particular task in a