But in .bazelrc, there is no "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX".
       That setting must exist for this script to function correctly.
EOF
  exit 1
fi
TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX"

# These flags share the user's gcloud credentials with the container, so that bazel
# inside the container can authenticate. Note: TF's CI does not have any credential

# limitations under the License.
# ==============================================================================
# Sourcing this enables local disk cache
if [[ $(uname -s) == "Darwin" ]]; then
  echo "Please note that using disk cache on macOS is not recommended because the"
  echo "cache can end up being pretty big and make the build process inefficient."
fi

    # Fixes "fatal: detected dubious ownership in repository" for Docker.
    git config --system --add safe.directory '*'
    git config --system protocol.file.allow always

    # Note that you could generate a list of all the affected targets with e.g.:
    # bazel query $(paste -sd "+" $BATS_FILE_TMPDIR/changed_files) --keep_going
    # Only shows Added, Changed, Modified, Renamed, and Type-changed files

    licenses = ["notice"],
)

# Currently pybind extension shared objects must use only C API headers since
# the C API has static initializers duplicated in the Python bindings. So we
# need a second rule that omits .cc files, in
# tensorflow/python:_pywrap_parallel_device.
filegroup(
    name = "lib_headers",
    srcs = ["parallel_device_lib.h"],
)

filegroup(
    name = "lib_sources",

    for details of what operations are supported and what are the differences
    from NumPy.
*   `tf.types.experimental.TensorLike` is a new `Union` type that can be used as
    type annotation for variables representing a Tensor or a value that can be
    converted to Tensor by `tf.convert_to_tensor`.

#
#   If you've clicked on a test result from our CI (via a dashboard or GitHub link),
#   click to "Invocation Details" and find BUILD_CONFIG, which will contain a TFCI
#   value in the "env_vars" list that you can choose to copy that environment.
#      Ex. 1: TFCI=py311,linux_x86_cuda,nightly_upload  (nightly job)
#      Ex. 2: TFCI=py39,linux_x86,rbe                   (continuous job)
#   Non-Googlers should replace "nightly_upload" or "rbe" with

These are issues that we believe are particularly well suited for outside
contributions, often because we probably won't get to them right now. If you
decide to start on an issue, leave a comment so that other people know that
you're working on it. If you want to help out, but not alone, use the issue
comment thread to coordinate.

# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

            "//tensorflow/compiler/tf2xla:xla_compiler",
        ],
        "//conditions:default": [],
    }) + if_tensorrt([
        "//tensorflow/compiler/tf2tensorrt:trt_convert_api",
    ]),
)

# Check that c_api_no_xla does not depend on xla.
check_deps(
    name = "c_api_no_xla_check_deps",
    disallowed_deps = ["//tensorflow/compiler/jit:xla_kernel_creator"],
    deps = [":c_api_no_xla"],
)

tf_cuda_library(

# unsupported flags to replace the release flags in your build command.
# However, please note that the old toolchain is no longer officially supported
# by TensorFlow and the unsupported configs will be removed soon b/299962977. We
# strongly recommend that you migrate to Clang as your compiler for TensorFlow
# Linux builds. Instructions are available in the official documentation: