tf_gcs_filesystem::Cleanup(filesystem_);
    delete filesystem_;
  }

  std::string GetURIForPath(absl::string_view path) {
    const std::string translated_name =
        tensorflow::io::JoinPath(root_dir_, path);
    return translated_name;
  }

  std::unique_ptr<TF_WritableFile, void (*)(TF_WritableFile* file)>
  GetWriter() {
    std::unique_ptr<TF_WritableFile, void (*)(TF_WritableFile * file)> writer(

    t.reset(x_raw);
  }

  AbstractOperationPtr check_numerics_op(ctx->CreateOperation());
  ForwardOperation forward_op;
  Status s = Reset(check_numerics_op.get(), "CheckNumerics",
                   /*raw_device_name=*/nullptr, &forward_op);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  if (isa<TracingOperation>(check_numerics_op.get())) {
    s = dyn_cast<TracingOperation>(check_numerics_op.get())

  std::unique_ptr<WritableFile> same_file;
  status = env_->NewAppendableFile(filepath, &same_file);
  if (!status.ok())
    GTEST_SKIP() << "NewAppendableFile() not supported: " << status;

  const std::string more_test_data("qwer");
  EXPECT_EQ(same_file->Append(more_test_data).code(), Code::OK);
  EXPECT_EQ(same_file->Flush().code(), Code::OK);
  EXPECT_EQ(same_file->Close().code(), Code::OK);

  }
}

void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value) {
  auto s = tensorflow::unwrap(op)->SetAttrInt(attr_name, value);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, float value) {
  auto s = tensorflow::unwrap(op)->SetAttrFloat(attr_name, value);
  if (!s.ok()) {

  if (name->empty())
    *name = "/";
  else if (name->back() != '/')
    name->push_back('/');
}

// A helper function to actually read the data from GCS.
static int64_t LoadBufferFromGCS(const std::string& path, size_t offset,
                                 size_t buffer_size, char* buffer,
                                 tf_gcs_filesystem::GCSFile* gcs_file,

  std::vector<IOSpec> v;
  for (const string& name : names) {
    v.push_back(IOSpec(name, DT_INVALID));
  }
  return v;
}

// 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

  int value = -1;
  TF_Status status;
  cache1.LookupOrCompute("a", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 0);
  EXPECT_EQ(num_compute_calls, 1);
  // re-read the same value, expect another lookup
  cache1.LookupOrCompute("a", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 1);
  EXPECT_EQ(num_compute_calls, 2);

    lru_list_.push_front(key);
    block->lru_iterator = lru_list_.begin();
  }

  // Check for inconsistent state. If there is a block later in the same file
  // in the cache, and our current block is not block size, this likely means
  // we have inconsistent state within the cache. Note: it's possible some
  // incomplete reads may still go undetected.
  if (block->data.size() < block_size_) {

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

  // Start a new function / execution context.
  string fn_name = "double";
  TF_ExecutionContext* graph_ctx =
      TF_CreateFunction(fn_name.c_str(), status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  auto* placeholder_t =

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