// Another renaming of local device
  const string second_name = "def";
  server_def.set_job_name(second_name);
  server_def.mutable_cluster()->mutable_job(0)->set_name(second_name);
  (*server_def.mutable_cluster()->mutable_job(0)->mutable_tasks())[0] =
      absl::StrCat(second_name, ":",
                   tensorflow::testing::PickUnusedPortOrDie());

  tensorflow::Env* env = tensorflow::Env::Default();
  {
    tensorflow::mutex_lock lock(mu);
    auto it = loaded_libs->find(library_filename);
    if (it != loaded_libs->end()) {
      lib_handle->lib_handle = it->second;
    } else {
      status->status =
          env->LoadDynamicLibrary(library_filename, &lib_handle->lib_handle);
      if (status->status.ok()) {
        TF_CHECK_OK(

    if (attr.first == "dtype") {
      if (attr.second.type() == tensorflow::DT_INT32) {
        found_dtype = true;
      } else {
        return false;
      }
    } else if (attr.first == "value") {
      if (attr.second.has_tensor() &&
          attr.second.tensor().int_val_size() == 1 &&
          attr.second.tensor().int_val(0) == v) {
        found_value = true;
      } else {

  if (iter == registry_.end()) {
    const string error_msg = "No gradient defined for op: " + op.op_name + ".";
    return errors::NotFound(error_msg);
  }
  gradient_function->reset(iter->second(op));
  return absl::OkStatus();
}

TapeTensor::TapeTensor(AbstractTensorHandle* handle) : handle_(handle) {
  handle_->Ref();
}
TapeTensor::TapeTensor(const TapeTensor& other) {
  handle_ = other.handle_;

TF_Buffer* TF_CoordinationServiceGetKeyValueWithTimeout(
    const char* key, int64_t key_size, int64_t timeout_seconds,
    TF_CoordinationServiceAgent* agent, TF_Status* status) {
  if (timeout_seconds <= 0) {
    status->status = absl::InvalidArgumentError(
        "TF_CoordinationServiceGetKeyValueWithTimeout invoked with invalid "
        "timeout_seconds <= 0.");
    return nullptr;
  }

                    /*input_shapes*/ {make_shape({3, 2}), make_shape({2, 4})},
                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({3, 4}));

  // Infer shape when second operand has unknown shape.
  CheckOutputShapes(matmul_op,
                    /*input_shapes*/ {make_shape({3, 2}), unknown_shape()},
                    /*input_tensors*/ {},

  block_size = kDefaultBlockSize;
  size_t max_bytes = kDefaultMaxCacheSize;
  uint64_t max_staleness = kDefaultMaxStaleness;

  // Apply the overrides for the block size (MB), max bytes (MB), and max
  // staleness (seconds) if provided.
  const char* block_size_env = std::getenv(kBlockSize);
  if (block_size_env && absl::SimpleAtoi(block_size_env, &value)) {
    block_size = value * 1024 * 1024;
  }

  TFE_Op* func_op = TFE_NewOp(ctx, func.name().data(), status);
  for (const auto& attr : func.attr()) {
    if (!status->status.ok()) return nullptr;
    SetOpAttrValueScalar(ctx, func_op, attr.second, attr.first.data(), status);
    if (!status->status.ok()) return nullptr;
  }
  return func_op;
}
}  // namespace

void TFE_ContextStartStep(TFE_Context* ctx) {

  // This signals that the block is removed, and should not be inadvertently
  // reinserted into the cache in UpdateLRU.
  entry->second->timestamp = 0;
  lru_list_.erase(entry->second->lru_iterator);
  lra_list_.erase(entry->second->lra_iterator);
  cache_size_ -= entry->second->data.capacity();
  block_map_.erase(entry);
}

  // Reading at offset file_size + 4 will read the second block (since the read
  // at file_size + 4 = 28 will be aligned to an offset of 16) but will return
  // OutOfRange because the offset is past the end of the 24-byte file.
  Status status = ReadCache(&cache, "", file_size + 4, 4, &out);
  EXPECT_EQ(status.code(), error::OUT_OF_RANGE);
  EXPECT_TRUE(second_block);
  // Reading the second full block will return 8 bytes, from a cache hit.