"Only TracingContext can be converted into a function."));
    return nullptr;
  }
  tsl::Set_TF_Status_from_Status(s,
                                 tracing_ctx->Finalize(unwrap(outputs), &func));
  TF_DeleteExecutionContext(ctx);
  return wrap(func);
}

TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,

    // Extract the resulting tensor.
    add_output2 = TF_OutputListGet(add_outputs, 0);
    TF_DeleteOutputList(add_outputs);
  }

  TF_DeleteAbstractTensor(arg0);
  TF_DeleteAbstractTensor(arg1);

  // Finalize the function by providing the returned values.
  TF_AbstractFunction* func;
  {
    // We want to return the output of both add operations, create a new list
    // and populate it.

  virtual Status AddParameter(DataType dtype, const PartialTensorShape& shape,
                              TracingTensorHandle**) = 0;

  // Finalize this context and make a function out of it. The context is in a
  // invalid state after this call and must be destroyed.
  virtual Status Finalize(OutputList* outputs, AbstractFunction**) = 0;

  // For LLVM style RTTI.
  static bool classof(const AbstractContext* ptr) {

          std::vector<string>(desc->colocation_constraints.begin(),
                              desc->colocation_constraints.end()));
    }
    status->status = desc->node_builder.Finalize(&desc->graph->graph, &ret,
                                                 /*consume=*/true);

    if (status->status.ok()) {
      // Run shape inference function for newly added node.

        } else {
          null_indices.insert(i);
        }
      }
      TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(func_ctx.get())
                             ->Finalize(&output_list, &func));
      scoped_func.reset(func);
      for (auto output : output_list.outputs) {
        output->Unref();
      }
      TF_RETURN_IF_ERROR(ctx->RegisterFunction(func));
    }

//
// Does not use the operation's definition to determine how many inputs should
// be attached. It is intended for use with TFE_OpGetFlatInput to inspect an
// already-finalized operation.
//
// Note that TFE_OpGetFlatInputCount and TFE_OpGetFlatInput operate on a flat
// sequence of inputs, unlike TFE_OpGetInputLength (for getting the length of a

          "Unable to cast input to GraphTensor");
    }
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {

    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    params->body_outputs[0] = {add2, 0};
    params->body_outputs[1] = params->body_inputs[1];

    // Finalize while loop
    TF_FinishWhile(params.get(), s_, &outputs[0]);
    EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  }

  // Define function, use it in graph, and run

  /// core TensorFlow upon registration (see the `TF_FilesystemPluginInfo`
  /// structure in Section 4).
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if transaction successfuly finalized.
  ///   * Must set `status` to `TF_NOT_FOUND` if token is invalid/not found
  ///   * Might use any other error value for `status` to signal other errors.
  int (*end_transaction)(const TF_Filesystem* filesystem,

# kernel registration - REGISTER_OP, REGISTER_KERNEL_BUILDER, etc.
#
# This setting is currently experimental. The 'v2' implementation does _not_
# correspond to a particular, finalized design; rather, it relates to
# developing one.
#
# The current aim of the 'v2' implementation is to allow 'unused' ops and
# kernels to be discarded by the linker (to the benefit of binary size).
bool_flag(