#endif  // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
}

#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
namespace {

// TODO(skyewm): make nodes in while loop unfetchable like in Python version
void TF_FinishWhileHelper(const TF_WhileParams* params, TF_Status* status,
                          TF_Output* outputs) {
  if (!ValidateInputWhileParams(*params, status)) return;

  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,

  }

  // Define function, use it in graph, and run
  DefineT(-1, {}, {{feed1, 0}, {feed2, 0}}, {outputs[0]}, {});
  TF_Operation* five = ScalarConst(5, host_graph_, s_, "five");
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({func_feed, five});
  Run({{func_feed, Int32Tensor(2)}}, func_op, 2 /*+=*/ + 5 + 1);

  // Verify input, output, and subset of edges in fdef.

        construct the layer with LSTM(recurrent_activation='hard_sigmoid') to
        fallback to 1.x behavior.
*   TensorFlow Lite
    *   Move from `tensorflow/contrib/lite` to `tensorflow/lite`.
    *   Add experimental Java API for injecting TensorFlow Lite delegates
    *   Add support for strings in TensorFlow Lite Java API.
*   `tf.contrib`:
    *   Add Apache Ignite Filesystem plugin to support accessing Apache IGFS.

  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,

    return;
  }
  status->status = coord_agent->WaitAtBarrier(
      barrier_id, absl::Milliseconds(barrier_timeout_in_ms), {});
}

void TFE_InitializeLocalOnlyContext(TFE_Context* ctx, int keep_alive_secs,
                                    const void* proto, size_t proto_len,
                                    TF_Status* status) {
  tensorflow::ServerDef server_def;
  if (!server_def.ParseFromArray(proto, proto_len)) {

// ----------------------------------------------------------------------------
namespace tf_gcs_filesystem {
// TODO(vnvo2409): Use partial reponse for better performance.
// TODO(vnvo2409): We could do some cleanups like `return TF_SetStatus`.
// TODO(vnvo2409): Refactor the filesystem implementation when
// https://github.com/googleapis/google-cloud-cpp/issues/4482 is done.
GCSFile::GCSFile(google::cloud::storage::Client&& gcs_client)

          if (op_it->second.output_tensor_info[j].GetID() == id) {
            found = true;
            Gradient* ones_like = nullptr;
            TF_RETURN_IF_ERROR(vspace.BuildOnesLike(
                op_it->second.output_tensor_info[j], &ones_like));
            (*result)[id].push_back(ones_like);
            break;
          }
        }
        if (!found) {
          return errors::Internal(

          close-issue-message: >
            This issue was closed because it has been inactive for 7 days since being marked as stale.
            Please reopen if you'd like to work on this further.
          days-before-pr-stale: 14
          days-before-pr-close: 14