"//conditions:default": "touch $@",  # Just a placeholder for Unix platforms
    }),
    visibility = ["//visibility:public"],
)

# The interface library (tensorflow_cc.dll.if.lib) for linking tensorflow DLL library (tensorflow_cc.dll) on Windows.
# To learn more about import library (called interface library in Bazel):

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cpu
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow_cpu"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=devel ci/official/containers/ml_build"

TFCI_BAZEL_COMMON_ARGS=
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=
TFCI_BUILD_PIP_PACKAGE_BASE_ARGS=
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG=
# Mostly meant to be used for Windows.
# For Windows, the same wheel is uploaded to both tensorflow_cpu, and tensorflow
TFCI_BUILD_PIP_PACKAGE_ADDITIONAL_WHEEL_NAMES=
TFCI_DOCKER_ARGS=
TFCI_DOCKER_ENABLE=
TFCI_DOCKER_IMAGE=
TFCI_DOCKER_PULL_ENABLE=
TFCI_DOCKER_REBUILD_ARGS=
TFCI_DOCKER_REBUILD_ENABLE=

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64
# Note: this is not set to "--cpu", because that changes the package name
# to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow"
# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=windows_x86_cpu_2022
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_BUILD_PIP_PACKAGE_ADDITIONAL_WHEEL_NAMES="tensorflow_cpu"
TFCI_OUTPUT_DIR=build_output
TFCI_FIND_BIN=C:/tools/msys64/usr/bin/find.exe
TFCI_LIB_SUFFIX="-cpu-windows-x86_64"
# auditwheel is not supported for Windows
TFCI_WHL_AUDIT_ENABLE=0
TFCI_WHL_AUDIT_PLAT=0

        issue found, _BigtableXYZDataset (of type DatasetV2) does not implement
        the _as_variant_tensor() of DatasetV1, when moving contrib.bigtable to
        tensorflow_io. Converting into DatasetV2 removes the overheads to
        maintain V1 while we are moving into TF 2.0.
    *   Add dataset ops to the graph (or create kernels in Eager execution)