// `StartExecute`s will deadlock).
  //
  // If `is_async=false` (constructor argument), `cancellation_manager` must
  // live until `Join` finishes. If `is_async=true` it must live until `Join` is
  // followed by `TFE_ContextAsyncWait` to clear pending operations. It will be
  // used to cancel all other operations if any fails.
  //

    well as externally on GitHub.

In a graphical form, the entire lifetime of a PR looks like

![image](https://github.com/tensorflow/tensorflow/assets/52792999/3eea4ca5-daa0-4570-b0b5-2a2b03a724a3)

### Contributor License Agreements

We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.

  // Requests that the worker thread execute the specified operation. Blocks
  // until the previously pending operation (a StartExecute without a Join) has
  // finished, if any.
  //
  // `cancellation_manager` must live until after `Join` finishes and pending
  // `is_async` operations finish. In addition to allowing the caller to cancel
  // the operation, its `StartCancel` method will be called if op execution

                                                   int keep_alive_secs,
                                                   const void* proto,
                                                   size_t proto_len,
                                                   TF_Status* status);

// A handle to a tensor on a device.
//
// Like a TF_Tensor, a TFE_TensorHandle refers to a tensor with a value, shape,

TF_CAPI_EXPORT extern void TFE_ContextUpdateServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status);

// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status,

//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test.py
//tensorflow/lite/python:interpreter
//tensorflow/lite/python:interpreter_test
//tensorflow/lite/python:interpreter.py

  AbstractContext* ctx_;
};

// Returns the number of elements in the gradient tensor.
int64_t TapeVSpace::NumElements(AbstractTensorHandle* tensor) const {
  // TODO(srbs): It seems like this is used only for performance optimization
  // and not for correctness. The only downside of keeping this 1 seems to be
  // that the gradient accumulation is unbounded and we will never

      "TFE_ContextSetServerDef not supported on mobile");
  return false;
#else   // !defined(IS_MOBILE_PLATFORM)
  bool is_alive;
  status->status =
      tensorflow::unwrap(ctx)->GetDistributedManager()->CheckRemoteAlive(
          worker_name, &is_alive);
  return is_alive;
#endif  // !IS_MOBILE_PLATFORM
}

TF_CAPI_EXPORT extern void TFE_ContextAsyncWait(TFE_Context* ctx,

  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  TFE_OpSetAttrType(fill_op, "T", TF_FLOAT);
  TFE_OpSetAttrType(fill_op, "Tshape", TF_INT32);

  float five = 5.0;
  TFE_TensorHandle* scalar = TestScalarTensorHandle(tfe_context_, five);
  TF_Tensor* scalarTensor = TFE_TensorHandleResolve(scalar, status_);
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  CheckOutputShapes(fill_op,

      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and