srcs = [":device_sources"],
    hdrs = [":device_headers"],
    visibility = ["//visibility:private"],
    deps = [
        ":parallel_device_lib",
        "//tensorflow/c:c_api",
        "//tensorflow/c:tf_status_helper",
        "//tensorflow/c/eager:c_api",
        "//tensorflow/c/eager:c_api_experimental",
        "//tensorflow/c/eager:tfe_tensorhandle_internal",
        "@com_google_absl//absl/strings",

filegroup(
    name = "checkpoint_reader_hdrs",
    srcs = [
        "checkpoint_reader.h",
        "tf_status_helper.h",
    ],
    visibility = ["//tensorflow:__subpackages__"],
)

tf_cuda_library(
    name = "tf_status_helper",
    srcs = ["tf_status_helper.cc"],
    hdrs = ["tf_status_helper.h"],
    visibility = ["//visibility:public"],
    deps = [
        ":tf_status_headers",

    # convert to modular filesystems everywhere
    visibility = ["//visibility:public"],
    deps = [
        ":filesystem_interface",
        "//tensorflow/c:tf_status_helper",
        "//tensorflow/c:tf_status_internal",
        "//tensorflow/core/platform:env",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:status",
        "@local_tsl//tsl/platform:errors",

        "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
        "@com_google_absl//absl/strings",
    ],
)

tf_cuda_cc_test(
    name = "c_api_remote_test",
    size = "medium",
    srcs = [
        "c_api_remote_test.cc",
    ],
    # TODO(b/136478427): Figure out how to correctly shut the server down
    args = ["--heap_check="],
    tags = [
        "no_windows",

        "//tensorflow/core/data/service:grpc_util",
        "//tensorflow/core/data/service:py_utils",
        "//tensorflow/core/data/service:server_lib",
        "//tensorflow/core/debug",
        "//tensorflow/core/distributed_runtime:server_lib",
        "//tensorflow/core/framework:full_type_util",
        "//tensorflow/core/function/runtime_client:runtime_client_cc",

        based server to register tf.function methods and a GRPC client to invoke
        remote registered methods. RPC APIs are intended for multi-client setups
        i.e. server and clients are started in separate binaries independently.

    *   Example usage to create server: ```python server =
        tf.distribute.experimental.rpc.Server.create("grpc", "127.0.0.1:1234")

        "nn_grad.h",
        "not_differentiable.h",
    ],
    visibility = ["//tensorflow/python:__pkg__"],
)

cc_library(
    name = "grad_test_helper",
    testonly = True,
    srcs = ["grad_test_helper.cc"],
    hdrs = ["grad_test_helper.h"],
    visibility = ["//visibility:private"],
    deps = [
        "//tensorflow/c/eager:gradient_checker",
        "//tensorflow/c/eager:gradients_internal",

        "@com_google_absl//absl/log",
    ],
    alwayslink = 1,
)

# Library implementing helper functionality, so that the above only contains
# the API implementation for modular filesystems.
cc_library(
    name = "posix_filesystem_helper",
    srcs = ["posix_filesystem_helper.cc"],
    hdrs = ["posix_filesystem_helper.h"],
    deps = [":copy_file"],
)

        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/types:variant",
    ],
)

cc_library(
    name = "gcs_helper",
    srcs = ["gcs_helper.cc"],
    hdrs = ["gcs_helper.h"],
    linkstatic = 1,
    deps = [
        "//tensorflow/c:env",
    ],
)

cc_library(
    name = "cleanup",
    hdrs = ["cleanup.h"],
)

cc_library(

# BEGIN TF CACHE HELPER OPTIONS
# Options when using remote execution
# WARNING: THESE OPTIONS WONT WORK IF YOU DO NOT HAVE PROPER AUTHENTICATION AND PERMISSIONS

# Use --config=tf_public_cache to try and use the TensorFlow public build cache