// capturing the return value in an 'auto' local variable. Most users will not
// need more sophisticated syntax than that.
//
// Example:
//   void func() {
//     FILE* fp = fopen("data.txt", "r");
//     if (fp == nullptr) return;
//     auto fp_cleaner = gtl::MakeCleanup([fp] { fclose(fp); });
//     // No matter what, fclose(fp) will happen.
//     DataObject d;

        if [[ ! -f $target ]] && [[ ! -d $target ]]; then
            echo "$i" >> errors.txt
        fi
        if [[ -e errors.txt ]]; then
            echo "Broken links found:"
            cat errors.txt
            rm errors.txt
            false
        fi
    done
}

@test "No duplicate files on Windows" {
    cat <<EOF

and deploy TensorFlow. This folder is typically used by continuous integration
(CI) tools to build and test TensorFlow whenever there is a change to the
code. This folder is broken into subfolders that represent the level of support

                             else 0)
    while k > bazel_comm_min_line_i:
      backtrack_line = log_lines[k]
      # Don't attempt to parse multi-line commands broken up by backslashes
      if 'bazel ' in backtrack_line and not backtrack_line.endswith('\\'):
        bazel_line = BAZEL_COMMAND_RE.search(backtrack_line)
        if bazel_line:

  TF_SetStatus(status, TF_OK, "");
}

static void NewWritableFile(const TF_Filesystem* filesystem, const char* path,
                            TF_WritableFile* file, TF_Status* status) {
  FILE* f = fopen(path, "w");
  if (f == nullptr) {
    TF_SetStatusFromIOError(status, errno, path);
    return;
  }

  file->plugin_file = new tf_writable_file::PosixFile({strdup(path), f});
  TF_SetStatus(status, TF_OK, "");

        if [[ ! -f $target ]] && [[ ! -d $target ]]; then
            echo "$i" >> errors.txt
        fi
        if [[ -e errors.txt ]]; then
            echo "Broken links found:"
            cat errors.txt
            rm errors.txt
            false
        fi
    done
}

@test "No duplicate files on Windows" {
    cat <<EOF

-		  ? ({ void *__s = (s); __bzero (__s, n); __s; })	      \
-		  : memset (s, c, n)))
-#  endif
-# endif
-
-/* GCC < 3.0 optimizes memset(s, 0, n) but not bzero(s, n).
-   The optimization is broken before EGCS 1.1.
-   GCC 3.0+ has __builtin_bzero as well, but at least till GCC 3.4
-   if it decides to call the library function, it calls memset
-   and not bzero.  */
-# if __GNUC_PREREQ (2, 91)

  Please upgrade your code to TensorFlow 2.0:
    * https://www.tensorflow.org/guide/migrate

  Or install the latest stable TensorFlow 1.X release:
    * `pip install -U "tensorflow==1.*"`

  Otherwise your code may be broken by the change.

  """)

# Make sure directory containing top level submodules is in
# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.

    to the layer came from a keras input.
*   Clean up `BatchNormalization` layer's `trainable` property to act like
    standard python state when it's used inside `tf.functions` (frozen at
    tracing time), instead of acting like a pseudo-variable whose updates *kind
    of sometimes* get reflected in already-traced `tf.function` traces.
*   Add the `Conv1DTranspose` layer.

we don't consider this as a vulnerability, given that models can exhaust
resources in many different ways and solutions exist to prevent this from
happening (e.g., rate limits, ACLs, monitors to restart broken servers).

### Model sharing

If the multitenant design allows sharing models, make sure that tenants and
users are aware of the security risks detailed here and that they are going to