try:
          __import__(short_package_name)
        except ImportError:
          logging.exception("error importing %s", short_package_name)
          failed_packages.append(package_name)

    if failed_packages:
      self.fail(
          "Failed to import"
          f" {len(failed_packages)}/{len(self.api_packages_v2)} packages"
          f" {version}:\n{failed_packages}"
      )

  if is_linux():
    if int(environ_cp.get('TF_NEED_TENSORRT', False)):
      cuda_libraries.append('tensorrt')
    if environ_cp.get('TF_NCCL_VERSION', None):
      cuda_libraries.append('nccl')
  if is_windows():
    if int(environ_cp.get('TF_NEED_TENSORRT', False)):
      cuda_libraries.append('tensorrt')
      print('WARNING: TensorRT support on Windows is experimental\n')

                 /*append_hash=*/false);
  tensorflow::FunctionDef fdef;
  ASSERT_TRUE(GetFunctionDef(func_, &fdef));
  ASSERT_EQ(string("long_func_name"), fdef.signature().name());
}

TEST_F(CApiFunctionTest, AppendHash) {
  DefineFunction("func_name_base", &func_, "Return something",
                 /*append_hash=*/true);
  tensorflow::FunctionDef fdef;

                      const TF_WritableFileOps* ops)
      : filename_(filename), file_(std::move(file)), ops_(ops) {}

  ~ModularWritableFile() override { ops_->cleanup(file_.get()); }

  Status Append(StringPiece data) override;
  Status Close() override;
  Status Flush() override;
  Status Sync() override;
  Status Name(StringPiece* result) const override;
  Status Tell(int64_t* position) override;

    control_output_nodes.push_back(&control_outputs[i]->node);
  }

  // Do the actual function creation.
  DCHECK(append_hash_to_fn_name <= 1);
  tensorflow::FunctionDef fdef;
  status->status = tensorflow::GraphToFunctionDef(
      fn_body->graph, fn_name, append_hash_to_fn_name != 0,
      /*set_stateful_from_nodes=*/true,
      /*copy_placeholder_attrs_from_nodes=*/true, body_nodes, input_tensors,

  ASSERT_TF_OK(status_);
  tf_writable_file::Append(file, content, 4, status_);
  ASSERT_TF_OK(status_);
  length = tf_writable_file::Tell(file, status_);
  ASSERT_EQ(length, 4);
  ASSERT_TF_OK(status_);
  tf_writable_file::Flush(file, status_);
  ASSERT_TF_OK(status_);
  ASSERT_TRUE(
      CompareWithServer(filepath, 0, 4, &gcs_file->gcs_client, status_));

  tf_writable_file::Append(file, content + 4, 4, status_);

int64_t Read(const TF_RandomAccessFile* file, uint64_t offset, size_t n,
             char* buffer, TF_Status* status);
}  // namespace tf_random_access_file

namespace tf_writable_file {
void Cleanup(TF_WritableFile* file);
void Append(const TF_WritableFile* file, const char* buffer, size_t n,
            TF_Status* status);
int64_t Tell(const TF_WritableFile* file, TF_Status* status);
void Flush(const TF_WritableFile* file, TF_Status* status);

  ///     have been written, potentially due to quota/disk space.
  ///   * Might use any other error value for `status` to signal other errors.
  void (*append)(const TF_WritableFile* file, const char* buffer, size_t n,
                 TF_Status* status);

  /// Returns the current write position in `*file`.
  ///

      # it, and print that list for debugging. Not really clear if this is
      # helpful since the only examples I've seen are enormous.
      bazel query "rdeps(kind(py_test, $(cat $BATS_TEST_TMPDIR/deps)), $dep, 1)"
    done < $BATS_TEST_TMPDIR/missing_deps
    exit 1
  fi
}

# The Python package is not allowed to depend on any CUDA packages.

//            name (OpDef.name) regexp [A-Z][A-Za-z0-9_.\\-/]*.
//            If `append_hash_to_fn_name` is false, `fn_name` must be distinct
//            from other function and operation names (at least those
//            registered in graphs where this function will be used).
//  append_hash_to_fn_name - Must be 0 or 1. If set to 1, the actual name