"//tensorflow/c:tf_status_internal",
        "//tensorflow/c/eager:c_api",
        "//tensorflow/c/eager:c_api_experimental",
        "//tensorflow/c/eager:tfe_cancellation_manager_internal",
        "//tensorflow/c/eager:tfe_op_internal",
        "//tensorflow/c/eager:tfe_tensorhandle_internal",
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
        "@com_google_absl//absl/types:optional",

}

# Note: this is excluded on the full code base, since any submitted code must
# have passed Google's internal style guidelines.
@test "Check formatting for C++ files" {
    skip "clang-format doesn't match internal clang-format checker"
    echo "clang-format is recommended. Here are the suggested changes:"
    echo "============================="

build --python_path="/usr/local/bin/python3"

# Build TensorFlow v2
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the

# export TFCI=py311,linux_x86,no_docker

# Advanced: Use Remote Build Execution (RBE) (internal developers only)
#
#   RBE dramatically speeds up builds and testing. It also gives you a
#   public URL to share your build results with collaborators. However,
#   it is only available to a limited set of internal TensorFlow developers.
#
#   RBE is incompatible with local caching, so you must remove

    # 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",
    ],
)

read/write/executions.

### Running a TensorFlow server

TensorFlow is a platform for distributed computing, and as such there is a
TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for
internal communication only. It is not built for use in untrusted environments
or networks.

For performance reasons, the default TensorFlow server does not include any

# Use lld as the linker
build --linkopt="-fuse-ld=lld"
build --linkopt="-lm"
build --linkopt="-Wl,--undefined-version"

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the

    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        "//tensorflow/c/eager:abstract_context",
        "//tensorflow/c/eager:gradients_internal",
    ],
)

cc_library(
    name = "math_grad",
    srcs = ["math_grad.cc"],
    hdrs = [
        "math_grad.h",
    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [

    ],
)

tf_cc_test(
    name = "ram_file_block_cache_test",
    size = "small",
    srcs = ["ram_file_block_cache_test.cc"],
    deps = [
        ":ram_file_block_cache",
        "//tensorflow/c:tf_status_internal",
        "//tensorflow/core:lib",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/platform:blocking_counter",
        "//tensorflow/core/platform/cloud:now_seconds_env",

}

# Note: this is excluded on the full code base, since any submitted code must
# have passed Google's internal style guidelines.
@test "Check formatting for C++ files" {
    skip "clang-format doesn't match internal clang-format checker"
    echo "clang-format is recommended. Here are the suggested changes:"
    echo "============================="