return;
      }
      // We have a convoluted scheme here: Using the C++ graph construction API
      // to add potentially many nodes to the graph without running the checks
      // (such as uniqueness of the names of nodes) we run with other functions
      // that add a node to the graph (like TF_FinishOperation).
      if (!g->name_map.insert(std::make_pair(n->name(), n)).second) {

  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).
  // Setting a larger thread pool does not help with the Swift caller, as we use

// TFE_NewTensorHandleFromDeviceMemory.
void DeallocatorWrapperFunc(void* data, size_t len, void* dlmt_vptr) {
  TFE_CallDLManagedTensorDeleter(dlmt_vptr);
}

// Checks whether the stride array matches the layout of compact, row-majored
// data.
bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr,
                                      int ndim) {

  EXPECT_EQ(out, B);
  EXPECT_EQ(calls, 4);
  // Remove the blocks from "a".
  cache.RemoveFile("a");
  // Both blocks from "b" should still be there.
  TF_EXPECT_OK(ReadCache(&cache, "b", 0, n, &out));
  EXPECT_EQ(out, b);
  TF_EXPECT_OK(ReadCache(&cache, "b", 8, n, &out));
  EXPECT_EQ(out, B);
  EXPECT_EQ(calls, 4);
  // The blocks from "a" should not be there.
  TF_EXPECT_OK(ReadCache(&cache, "a", 0, n, &out));

                               v2_reader_->value().size()))
        << entry.InitializationErrorString();
    for (int i = 0; i < entry.slices_size(); ++i) {
      const auto& slice_proto = entry.slices(i);
      CHECK(filtered_keys
                .insert(EncodeTensorNameSlice(
                    string(v2_reader_->key()) /* full var's name */,
                    TensorSlice(slice_proto)))
                .second);
    }

  new_entry->lra_iterator = lra_list_.begin();
  new_entry->timestamp = timer_seconds_();
  block_map_.emplace(std::make_pair(key, new_entry));
  return new_entry;
}

// Remove blocks from the cache until we do not exceed our maximum size.
void RamFileBlockCache::Trim() {
  while (!lru_list_.empty() && cache_size_ > max_bytes_) {
    RemoveBlock(block_map_.find(lru_list_.back()));
  }
}

  for (const auto& status : statuses)
    EXPECT_PRED2(UnimplementedOrReturnsCode, status, Code::OK);
}

TEST_P(ModularFileSystemTest, TestFilesExistAllFailureModes) {
  // if reordering these, make sure to reorder checks at the end
  const std::vector<std::string> filenames = {
      GetURIForPath("a_dir"),
      GetURIForPath("a_file"),
      GetURIForPath("a_file/a_new_file"),
      GetURIForPath("file_not_found"),
  };

}

// Converts `noutputs` and `outputs` into `outputs_tensors` and does various
// checks while doing so.
Status ProcessOutputs(const TF_Graph* fn_body, const char* fn_name,
                      int noutputs, const TF_Output* outputs,
                      std::vector<OutputTensor>* output_tensors)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  output_tensors->reserve(noutputs);
  for (int i = 0; i < noutputs; ++i) {

            tensorflow::ThreadOptions(), "parallel_device_execute",
            std::bind(&DeviceThread::Run, this))) {}
  ~DeviceThread();

  // Requests that the worker thread execute the specified operation. Blocks
  // until the previously pending operation (a StartExecute without a Join) has
  // finished, if any.
  //
  // `cancellation_manager` must live until after `Join` finishes and pending

#include "tensorflow/c/tf_status_internal.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {

// Checks that all schemes provided by a plugin are valid.
// TODO(b/139060984): More validation could be done here, based on supported
// charset, maximum length, etc. Punting it for later.
static Status ValidateScheme(const char* scheme) {