export_dir, tag_set, &bundle);
  if (!status->status.ok()) return nullptr;

  // Create a TF_Graph from the MetaGraphDef. This is safe as long as Session
  // extends using GraphDefs. The Graph instance is different, but equivalent
  // to the one used to create the session.
  //
  // TODO(jhseu): When Session is modified to take Graphs instead of

  // Set device to CPU since there is no version of split for int32 on GPU
  // TODO(iga): Convert all these helpers and tests to use floats because
  // they are usually available on GPUs. After doing this, remove TF_SetDevice
  // call in c_api_function_test.cc
  TF_SetDevice(desc, "/cpu:0");
  *op = TF_FinishOperation(desc, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  ASSERT_NE(*op, nullptr);
}

BASE="$1"
find "${BASE}" -type l | \
  while read l ; do
    if [[ "$(readlink "$l")" == \.\./* ]]; then
      CANONICAL="$(readlink "$l")";
      rm "$l";
      ln -s "${CANONICAL}" "$l"
    fi

    --enable_bzlmod=false \
    //:requirements_"$VERSION"_release.update
  sed -i '/^#/d' requirements_lock_"$VERSION".txt
  mv "requirements_lock_"$VERSION".txt" ../../../requirements_lock_"$VERSION".txt
done

    TensorFlow models, please demonstrate an exploit against one of Alphabet's
    models in <https://tfhub.dev/>
  - An explanation of who can exploit this vulnerability, and what they gain
    when doing so. Write an attack scenario that demonstrates how your issue
    violates the use cases and security assumptions defined in the threat model.
    This will help us evaluate your report quickly, especially if the issue is

#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {

// Checks that all schemes provided by a plugin are valid.
// TODO(b/139060984): More validation could be done here, based on supported
// charset, maximum length, etc. Punting it for later.
static Status ValidateScheme(const char* scheme) {
  if (scheme == nullptr)
    return errors::InvalidArgument(

// TODO(vnvo2409): We could do some cleanups like `return TF_SetStatus`.
// TODO(vnvo2409): Refactor the filesystem implementation when
// https://github.com/googleapis/google-cloud-cpp/issues/4482 is done.
GCSFile::GCSFile(google::cloud::storage::Client&& gcs_client)
    : gcs_client(gcs_client), block_cache_lock() {
  const char* append_mode = std::getenv(kAppendMode);

  /// won't be registered anymore is if this function gets called by core
  /// TensorFlow and the `TF_Filesystem*` object is destroyed. However, due to
  /// registration being done in a static instance of `Env`, the destructor of
  /// `FileSystem` is never called (see
  /// https://github.com/tensorflow/tensorflow/issues/27535). In turn, this

        d="$${d#*external/farmhash_archive/src}"
        d="$${d#*external/$${extname}/}"
        d="$${d#_virtual_includes/*/}"
      fi

      mkdir -p "$@/$${d}"
      cp "$${f}" "$@/$${d}/"
    done
    """,
    tags = ["manual"],
    visibility = ["//visibility:public"],
)

# copybara:comment_begin(oss-only)
copy_file(
    name = "root_init_gen",
    src = select({

// Below here we do the gradient algorithm. It works as follows:
//
// First we filter the tape to just the subset of operations we want to
// differentiate. In the process of doing so we count how many times each Tensor
// is used as an input to an op (so we know when we're done computing gradients
// for that Tensor). We also count, for each tape entry, how many of its output
// Tensors need gradients to be computed (Tensors which are not used do not need