void TF_DeleteOutputList(TF_OutputList* o) { delete unwrap(o); }
void TF_OutputListSetNumOutputs(TF_OutputList* o, int num_outputs,
                                TF_Status* s) {
  unwrap(o)->expected_num_outputs = num_outputs;
  unwrap(o)->outputs.clear();
  unwrap(o)->outputs.resize(num_outputs);
}
int TF_OutputListNumOutputs(TF_OutputList* o) {
  return unwrap(o)->outputs.size();
}

      TF_NewStatus(), TF_DeleteStatus);
  TFE_Context* eager_ctx =
      TF_ExecutionContextGetTFEContext(wrap(ctx), status.get());
  TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
  TFE_TensorHandle* input_eager =
      TestTensorHandleWithDims<T, datatype>(eager_ctx, data, dims, num_dims);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

  tensorflow::unwrap(ctx)->StartStep();
}

void TFE_ContextEndStep(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->EndStep();
}

const TFE_OpAttrs* TFE_OpGetAttrs(const TFE_Op* op) {
  return tensorflow::wrap(tensorflow::unwrap(op)->GetOpAttrs());
}

void TFE_OpAddAttrs(TFE_Op* op, const TFE_OpAttrs* attrs) {
  tensorflow::unwrap(op)->AddAttrs(tensorflow::unwrap(attrs));
}

  tensorflow::unwrap(ctx)->SetExecutorForThread(executor->executor());
}

TFE_Executor* TFE_ContextGetExecutorForThread(TFE_Context* ctx) {
  return new TFE_Executor(&tensorflow::unwrap(ctx)->Executor());
}

void TFE_HostAddressSpace(TFE_Context* ctx, TF_Buffer* buf) {
  auto address_space = tensorflow::DeviceNameUtils::AddressSpace(
      tensorflow::unwrap(ctx)->HostCPUParsedName());

AbstractContext* BuildFunction(const char* fn_name) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_ExecutionContext* graph_ctx = TF_CreateFunction(fn_name, status.get());
  return unwrap(graph_ctx);
}

Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params) {

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;
  }
  tensorflow::TensorHandle* handle =
      tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h));
  if (handle->Type() != TensorHandle::LOCAL) {
    status->status = tensorflow::errors::InvalidArgument(
        "DLPack doesn't support ", handle->TypeString(), " tensor");
    return nullptr;
  }

    return;
  }
  status->status = tensorflow::unwrap(ctx)->EnableCollectiveOps(server_def);
}

TF_CAPI_EXPORT extern void TFE_AbortCollectiveOps(TFE_Context* ctx,
                                                  TF_Status* status) {
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  auto collective_executor_handle = context->GetCollectiveExecutorHandle();

  int real_num_outputs = expected_max_outputs;
  TFE_OpSetCancellationManager(op_.get(), wrap(cancellation_manager_), status);
  if (TF_GetCode(status) != TF_OK) return;

  // unwrap op_ and set step_id only if valid step id value was set.
  // Currently only required for non-TFRT use cases, e.g., EagerOp.
  if (step_id_.has_value()) {
    tensorflow::unwrap(op_.get())->SetStepId(step_id_.value());
  }

)

# Currently pybind extension shared objects must use only C API headers since
# the C API has static initializers duplicated in the Python bindings. So we
# need a second rule that omits .cc files, in
# tensorflow/python:_pywrap_parallel_device.
filegroup(
    name = "lib_headers",
    srcs = ["parallel_device_lib.h"],
)

filegroup(
    name = "lib_sources",
    srcs = ["parallel_device_lib.cc"],
)

filegroup(

import inspect as _inspect
import os as _os
import site as _site
import sys as _sys

# Do not remove this line; See https://github.com/tensorflow/tensorflow/issues/42596
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow  # pylint: disable=unused-import
from tensorflow.python.tools import module_util as _module_util
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader