auto& kUnknownDim = shape_inference::InferenceContext::kUnknownDim;
auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank;

// GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index
// into the list of outputs for the operation.
class GraphTensor : public TracingTensorHandle {
 public:
  explicit GraphTensor(TF_Output output, TF_Graph* graph)
      : TracingTensorHandle(kGraph), output_(output), graph_(graph) {}

// 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> {
  typedef std::pair<string, string> Base;

      return;
    }
  }
  TF_VLog(3, "GcsWritableFile: %s with existing file %s", path,
          temp_file_name.c_str());
  TF_SetStatus(status, TF_OK, "");
}

// TODO(vnvo2409): We could download into a local temporary file and use
// memory-mapping.
void NewReadOnlyMemoryRegionFromFile(const TF_Filesystem* filesystem,
                                     const char* path,

    TF_SetStatus(status, TF_OK, "");
    return 0;
  }
  if (!IsCacheEnabled() || (n > max_bytes_)) {
    // The cache is effectively disabled, so we pass the read through to the
    // fetcher without breaking it up into blocks.
    return block_fetcher_(filename, offset, n, buffer, status);
  }
  // Calculate the block-aligned start and end of the read.
  size_t start = block_size_ * (offset / block_size_);

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_Tensor* result_tensor = TFE_TensorHandleResolve(result_t, status.get());

  // Copy Tensor data into an array.
  float result_data[4] = {0};
  memcpy(&result_data[0], TF_TensorData(result_tensor),
         TF_TensorByteSize(result_tensor));

  int data_len = 4;  // length of result_data

  env->SetNowSeconds(now);
  tf_gcs_filesystem::RamFileBlockCache cache(
      8, 32, 1 /* max staleness */, fetcher,
      [&env]() { return env->NowSeconds(); });
  // Read three blocks into the cache, and advance the timestamp by one second
  // with each read. Start with a block of "a" at the current timestamp `now`.
  TF_EXPECT_OK(ReadCache(&cache, "a", 0, 1, &out));

  EXPECT_EQ(0, TF_OperationNumControlOutputs(neg));
  EXPECT_EQ(0, TF_OperationGetControlOutputs(neg, control_ops, 100));

  // Import it again, with an input mapping, return outputs, and a return
  // operation, into the same graph.
  TF_DeleteImportGraphDefOptions(opts);
  opts = TF_NewImportGraphDefOptions();
  TF_ImportGraphDefOptionsSetPrefix(opts, "imported2");
  TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});

  TF_DeleteTensor(t);
  EXPECT_EQ(2, product[0]);
  EXPECT_EQ(4, product[1]);
  EXPECT_EQ(6, product[2]);
  EXPECT_EQ(8, product[3]);

  // When we turn on grappler, confirm that the tf.Add has been rewritten into a
  // tf.Mul.
  // This capability of checking the executed op names is currently only enabled
  // for TFRT debug build, for performance and simplicity reasons.
  if (use_tfrt) {