op_->node_builder.Attr(attr_name, func_name);
    return absl::OkStatus();
  }
  absl::Status SetAttrTensor(const char* attr_name,
                             AbstractTensorInterface* tensor) override {
    return tensorflow::errors::Unimplemented(
        "SetAttrTensor has not been implemented yet.");
  }
  absl::Status SetAttrStringList(const char* attr_name,

                                           size_t length) = 0;
  virtual absl::Status SetAttrTensor(const char* attr_name,
                                     AbstractTensorInterface* tensor) = 0;
  virtual absl::Status SetAttrStringList(const char* attr_name,
                                         const void* const* values,
                                         const size_t* lengths,

operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel...

  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle(ctx);
  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle(ctx);
  std::vector<TFE_TensorHandle*> handles_task0;
  if (heavy_load_on_streaming_rpc) {
    // Send 50 tensor copy requests to simulate that there have been some RPC
    // requests been enqueued.
    for (int i = 0; i < 50; ++i) {
      handles_task0.push_back(TestMatrixTensorHandle(ctx));
    }
  }

#include "tensorflow/core/common_runtime/graph_constructor.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/platform/mutex.h"

        "//tensorflow/core:reader_base",
        "//tensorflow/core:script_ops_op_lib",
        "//tensorflow/distribute/experimental/rpc/kernels:rpc_ops",
        "//tensorflow/dtensor/cc:dtensor_device_cc",
        "//tensorflow/dtensor/cc:tensor_layout",
        "//tensorflow/lite/c:common",
        "//tensorflow/lite/core/api",
        "//tensorflow/lite/delegates/flex:delegate",

#include "tensorflow/core/framework/graph_to_functiondef.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/tensor.pb.h"  // NOLINT
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/platform/base64.h"
#include "tensorflow/core/platform/strcat.h"

// Specification for an expected edge.
// src is either:
// - input name (as it appears in FunctionDef)
// - name of output tensor (in nested "add:z:0" format)
// dst is either:
// - output name (as it appears in FunctionDef)
// - <name_of_node>:<index_of_this_input_into_node> (this looks the same as
//      output tensor naming, but it the index is actually an input index)
struct EdgeSpec : public std::pair<string, string> {

    EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(handle_1));
    EXPECT_EQ(0, TFE_TensorHandleNumDims(handle_1, status));
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

    // Read the value of tensor handle `handle_1`.
    float value = 0.0f;
    TF_Tensor* t = TFE_TensorHandleResolve(handle_1, status);
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    ASSERT_EQ(sizeof(float), TF_TensorByteSize(t));

TFE_TensorHandle* TFE_NewTensorHandleFromTensor(TFE_Context* ctx, TF_Tensor* t,
                                                TF_Status* status) {
  return tensorflow::wrap(
      tensorflow::unwrap(ctx)->CreateLocalHandle(t->tensor));
}

TFE_TensorHandle* TFE_CreatePackedTensorHandle(TFE_Context* ctx,
                                               TFE_TensorHandle** handles,
                                               int* num_handles,