/// Deletes the directory specified by `path` and all its contents.
  ///
  /// This is accomplished by traversing directory tree rooted at `path` and
  /// deleting entries as they are encountered, from leaves to root. Each plugin
  /// is free to choose a different approach which obtains similar results.
  ///
  /// On successful deletion, `status` must be `TF_OK` and `*undeleted_files`

  TF_Input three_port;
  EXPECT_EQ(1,
            TF_OperationOutputConsumers(TF_Output{three, 0}, &three_port, 1));
  EXPECT_EQ(add, three_port.oper);
  EXPECT_EQ(1, three_port.index);

  // Serialize to GraphDef.
  GraphDef graph_def;
  ASSERT_TRUE(GetGraphDef(graph, &graph_def));

  // Validate GraphDef is what we expect.
  bool found_placeholder = false;

    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    const char* device_name = TFE_OpGetDevice(matmul, status);
    ASSERT_TRUE(strstr(device_name, "GPU:0") != nullptr);

    TFE_OpSetDevice(matmul, "CPU:0", status);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    device_name = TFE_OpGetDevice(matmul, status);
    ASSERT_TRUE(strstr(device_name, "CPU:0") != nullptr);
  }

}

TEST_P(ModularFileSystemTest, TestFilesExistsNoFiles) {
  const std::vector<std::string> filenames = {};
  EXPECT_TRUE(env_->FilesExist(filenames, /*status=*/nullptr));

  std::vector<Status> statuses;
  EXPECT_TRUE(env_->FilesExist(filenames, &statuses));
  EXPECT_TRUE(statuses.empty());
}

TEST_P(ModularFileSystemTest, TestStatEmptyFile) {
  const std::string filepath = GetURIForPath("a_file");

build:mkl --define=build_with_mkl=true --define=enable_mkl=true
build:mkl --define=tensorflow_mkldnn_contraction_kernel=0
build:mkl --define=build_with_openmp=true
build:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
build:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
build:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0

  EXPECT_EQ(op_def.input_arg_size(), 0);
  EXPECT_EQ(op_def.output_arg_size(), 1);
  EXPECT_TRUE(op_def.is_stateful());

  TF_DeleteBuffer(buffer);
}

void AssertEqual(TF_Function* f1, TF_Function* f2) {
  string s1, s2;
  tensorflow::FunctionDef fdef1, fdef2;
  ASSERT_TRUE(GetFunctionDef(f1, &fdef1));
  ASSERT_TRUE(GetFunctionDef(f2, &fdef2));
  SerializeToStringDeterministic(fdef1, &s1);

        "dynamic_loaded_kernels": "true",
        "framework_shared_object": "true",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "using_rocm_hipcc",
    define_values = {"using_rocm_hipcc": "true"},
)

config_setting(
    name = "override_eigen_strong_inline",
    define_values = {"override_eigen_strong_inline": "true"},
    visibility = ["//visibility:public"],
)

  else:
    question += ' [y/N]: '

  var = environ_cp.get(var_name)
  if var is not None:
    var_content = var.strip().lower()
    true_strings = ('1', 't', 'true', 'y', 'yes')
    false_strings = ('0', 'f', 'false', 'n', 'no')
    if var_content in true_strings:
      var = True
    elif var_content in false_strings:
      var = False
    else:
      raise UserInputError(

typedef struct TF_AttrMetadata {
  // A boolean: 1 if the attribute value is a list, 0 otherwise.
  unsigned char is_list;

  // Length of the list if is_list is true. Undefined otherwise.
  int64_t list_size;

  // Type of elements of the list if is_list != 0.
  // Type of the single value stored in the attribute if is_list == 0.
  TF_AttrType type;