"//conditions:default": [],
    }),
    tags = [
        "no_cuda_asan",  # TODO(b/181771536)
        "no_windows",  # TODO(b/155444728)
    ],
    # We must ensure that the dependencies can be dynamically linked since
    # the shared library must be able to use core:framework.
    deps = [
        ":c_api",
        ":c_api_internal",
        ":c_test_util",
        ":test_op_kernel",
        ":tf_buffer",

      --with-gcc-major-version-only \
      --with-linker-hash-style="gnu" \
      && \
      make -j$(nproc) && \
      make install


# Create the devtoolset libstdc++ linkerscript that links dynamically against
# the system libstdc++ 4.4 and provides all other symbols statically.
# Note that the installation path for libstdc++ here is ${TARGET}/usr/lib64/
mv "${TARGET}/usr/lib64/libstdc++.so.${LIBSTDCXX_VERSION}" \

This means that a CUDA target was probably included via an is_static condition,
used when targeting platforms like Windows where we build statically instead
of dynamically. Here's the output from bazel query:
EOF
  cat $BATS_TEST_TMPDIR/out
  [[ ! -s $BATS_TEST_TMPDIR/out ]]
}

@test "All tensorflow.org/code links point to real files" {

// supported on embedded on mobile and embedded platforms and will fail if
// called.
//
// Pass "library_filename" to a platform-specific mechanism for dynamically
// loading a library. The rules for determining the exact location of the
// library are platform-specific and are not documented here.
//
// On success, returns the newly created library handle and places OK in status.

This means that a CUDA target was probably included via an is_static condition,
used when targeting platforms like Windows where we build statically instead
of dynamically. Here's the output from bazel query:
EOF
  cat $BATS_TEST_TMPDIR/out
  [[ ! -s $BATS_TEST_TMPDIR/out ]]
}

@test "All tensorflow.org/code links point to real files" {

TF_CAPI_EXPORT extern void TF_JoinThread(TF_Thread* thread);

// \brief Load a dynamic library.
//
// Pass "library_filename" to a platform-specific mechanism for dynamically
// loading a library. The rules for determining the exact location of the
// library are platform-specific and are not documented here.
//
// On success, place OK in status and return the newly created library handle.

#     verbose_logs:     Show all compiler warnings during build.
#     monolithic:       Build all TF C++ code into a single shared object.
#     dynamic_kernels:  Try to link all kernels dynamically (experimental).
#     dbg:              Build with debug info
#
# TF version options;
#     v2: Build TF v2
#
# Feature and Third party library support options:
#     xla:          Build TF with XLA

bool_setting(
    name = "oss_setting",
    build_setting_default = if_oss(True, False),
    visibility = ["//visibility:private"],
)

# Setting to use when loading kernels dynamically
config_setting(
    name = "dynamic_loaded_kernels",
    define_values = {
        "dynamic_loaded_kernels": "true",
        "framework_shared_object": "true",
    },
    visibility = ["//visibility:public"],

        the input layer.

## Bug Fixes and Other Changes

### TF Core:

*   Set `tf2_behavior` to 1 to enable V2 for early loading cases.
*   Add `execute_fn_for_device function` to dynamically choose the
    implementation based on underlying device placement.
*   Eager:
    *   Add `reduce_logsumexp` benchmark with experiment compile.
    *   Give `EagerTensor`s a meaningful `__array__` implementation.

// TF_Library holds information about dynamically loaded TensorFlow plugins.
typedef struct TF_Library TF_Library;

// Load the library specified by library_filename and register the ops and
// kernels present in that library.
//
// Pass "library_filename" to a platform-specific mechanism for dynamically
// loading a library. The rules for determining the exact location of the