std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      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 =

      s, tracing_ctx->AddParameter(static_cast<DataType>(dtype), partial_shape,
                                   &t));
  return wrap(t);
}

void TF_DeleteExecutionContext(TF_ExecutionContext* c) { unwrap(c)->Release(); }

TF_AbstractOp* TF_NewAbstractOp(TF_ExecutionContext* c) {
  return wrap((unwrap(c)->CreateOperation()));
}

void TF_DeleteAbstractOp(TF_AbstractOp* op) { unwrap(op)->Release(); }

      return device_.shall_pin_to_this_device(tensorflow::wrap(op), &status);
    }
    return errors::Unimplemented("No custom device pinning implementation.");
  }

 private:
  TFE_Context* context_;
  TFE_CustomDevice device_;
  void* info_;
  string name_;
};

// An adapter which wraps the shape/data produced by C custom devices and uses
// it to implement custom device methods.

}

void TFE_ContextOptionsSetTfrt(TFE_ContextOptions* options, bool use_tfrt) {
  options->use_tfrt = use_tfrt;
}

TFE_CancellationManager* TFE_NewCancellationManager() {
  return tensorflow::wrap(new tensorflow::CancellationManager);
}

void TFE_CancellationManagerStartCancel(
    TFE_CancellationManager* cancellation_manager) {
  tensorflow::unwrap(cancellation_manager)->StartCancel();
}

  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TFE_TensorHandle* result_t =
      TF_AbstractTensorGetEagerTensor(wrap(t), status.get());
  TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
  *result_tensor = TFE_TensorHandleResolve(result_t, status.get());
  return StatusFromTF_Status(status.get());
}

// "/job:localhost/replica:0/task:0/device:CUSTOM:0".
//
// The custom device defines copy operations for moving TensorHandles on and
// off, and an execution operation for named operations. Often execution will
// simply wrap op execution on one or more physical devices.
//
// device_info is an opaque caller-defined type stored with the custom device
// which is passed to the functions referenced in the TFE_CustomDevice struct

  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,
      MemoryReleaser memory_releaser, void* memory_releaser_arg) = 0;

  // Create a handle to wrap and manage a Tensor
  virtual ImmediateExecutionTensorHandle* CreateLocalHandle(
      AbstractTensorInterface* t) = 0;
  // Copy the handle to another device.
  virtual ImmediateExecutionTensorHandle* CopyTensorHandleToDevice(

  tensorflow::Tensor tensor(dtype, tensorflow::TensorShape({}));
  std::memcpy(tensorflow::TensorCApi::Buffer(tensor)->data(), data, len);

  status->status = absl::OkStatus();
  return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(tensor));
}

// Set server_def on the context, possibly updating it.
TF_CAPI_EXPORT extern void TFE_EnableCollectiveOps(TFE_Context* ctx,

    if (TF_GetCode(status) != TF_OK) return;
  }
  std::vector<TFE_TensorHandle*> unwrapped_results(expected_max_outputs);
  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.

    --hash=sha256:4d4987ce51a49370ea65c0bfd2234e8ce80a12780820d9dc462597a6e60d0841
    # via
    #   -r requirements.in
    #   astunparse
wrapt==1.16.0 \
    --hash=sha256:0d2691979e93d06a95a26257adb7bfd0c93818e89b1406f5a28f36e0d8c1e1fc \
    --hash=sha256:14d7dc606219cdd7405133c713f2c218d4252f2a469003f8c46bb92d5d095d81 \