# 3. Add modifiers. Some modifiers for local execution are:
#      Ex. disk_cache -- Use a local cache
#      Ex. public_cache -- Use TF's public cache (read-only)
#      Ex. public_cache_push -- Use TF's public cache (read and write, Googlers only)
#      Ex. rbe        -- Use RBE for faster builds (Googlers only; see below)
#      Ex. no_docker  -- Disable docker on enabled platforms

# limitations under the License.
# ==============================================================================
# Sourcing this enables Bazel remote cache (read and write)
# Note that "_push" cache configs write to GCS buckets and require
# authentication. If you are not a Googler, source "public_cache" to enable the
# public read-only cache.
# The cache configs are different for MacOS and Linux

variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
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

*   Fixes an integer overflow in TFLite
    ([CVE-2022-23559](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23559))
*   Fixes a dangerous OOB write in TFLite
    ([CVE-2022-23561](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23561))
*   Fixes a vulnerability leading to read and write outside of bounds in TFLite
    ([CVE-2022-23560](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23560))

    srcs = [":tf_custom_op_library_additional_deps.dll"],
    output_group = "def_file",
)

# Filter the DEF file to reduce the number of symbols to 64K or less.
# Note that we also write the name of the pyd file into DEF file so that
# the dynamic libraries of custom ops can find it at runtime.
genrule(
    name = "tensorflow_filtered_def_file",
    srcs = [":tensorflow_def_file"],