# Recommended: use a local+remote cache.
#
#   Bazel will cache your builds in tensorflow/build_output/cache,
#   and will also try using public build cache results to speed up
#   your builds. This usually saves a lot of time, especially when
#   re-running tests. However, note that:
#
#    - New environments like new CUDA versions, changes to manylinux,
#      compilers, etc. can cause undefined behavior such as build failures

        "//tensorflow/cc/saved_model:tag_constants",
        "//tensorflow/compiler/jit",
        "//tensorflow/core:array_ops_op_lib",
        "//tensorflow/core:bitwise_ops_op_lib",
        "//tensorflow/core:control_flow_ops_op_lib",
        "//tensorflow/core:core_cpu_internal",
        "//tensorflow/core:direct_session",
        "//tensorflow/core:framework",
        "//tensorflow/core:framework_internal",

        ":tape",
        "//tensorflow/core/common_runtime/eager:attr_builder",
        "//tensorflow/core/lib/llvm_rtti",
        "//tensorflow/core/platform:errors",
        "@com_google_absl//absl/container:flat_hash_map",
        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "unified_api_testutil",
    testonly = 1,
    srcs = [
        "unified_api_testutil.cc",
    ],

*   EagerTensor now supports numpy buffer interface for tensors.
*   Add toggles `tf.enable_control_flow_v2()` and `tf.disable_control_flow_v2()`
    for enabling/disabling v2 control flow.
*   Enable v2 control flow as part of `tf.enable_v2_behavior()` and
    `TF2_BEHAVIOR=1`.
*   AutoGraph translates Python control flow into TensorFlow expressions,
    allowing users to write regular Python inside `tf.function`-decorated

          tf_additional_profiler_deps()) + if_xla_available([
        "//tensorflow/compiler/aot:tfcompile_lib",
    ]) + if_static(extra_deps = [
        "//tensorflow/core/platform:tensor_float_32_utils",
        "//tensorflow/core/platform:enable_tf2_utils",
    ]) + if_oss([
        "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
        "//tensorflow/core/distributed_runtime/rpc:grpc_session",

        "//tensorflow/c/experimental/ops:nn_ops",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/platform:tensor_float_32_utils",
    ] + if_libtpu(
        if_false = ["//tensorflow/compiler/mlir/tensorflow/c:mlir_c_api_registration"],
        if_true = [],
    ),
)

tf_cuda_cc_test(
    name = "math_grad_test",

# END TF TEST SUITE OPTIONS

# START CROSS-COMPILE CONFIGS
# Set execution platform to Linux x86
# Note: Lot of the "host_" flags such as "host_cpu" and "host_crosstool_top"
# flags seem to be actually used to specify the execution platform details. It
# seems it is this way because these flags are old and predate the distinction