TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

*   Added **categorical data** processing layers:
    *   `IntegerLookup` & `StringLookup`: build an index of categorical feature
        values
    *   `CategoryEncoding`: turn integer-encoded categories into one-hot,
        multi-hot, or tf-idf encoded representations
    *   `CategoryCrossing`: create new categorical features representing
        co-occurrences of previous categorical feature values

  def maybe_encode_env(env):
    """Encodes unicode in env to str on Windows python 2.x."""
    if not is_windows() or sys.version_info[0] != 2:
      return env
    for k, v in env.items():
      if isinstance(k, unicode):
        k = k.encode('ascii')
      if isinstance(v, unicode):
        v = v.encode('ascii')
      env[k] = v
    return env

+++ b/sysdeps/unix/sysv/linux/if_index.c
@@ -38,12 +38,19 @@ __if_nametoindex (const char *ifname)
   return 0;
 #else
   struct ifreq ifr;
+  if (strlen (ifname) >= IFNAMSIZ)
+    {
+      __set_errno (ENODEV);
+      return 0;
+    }
+
+  strncpy (ifr.ifr_name, ifname, sizeof (ifr.ifr_name));
+
   int fd = __opensock ();
 
   if (fd < 0)
     return 0;
 

  StatusPtr first_bad_status(nullptr);

  for (const auto& dt : device_threads_) {
    StatusPtr async_wait_status(TF_NewStatus());
    dt->AsyncWait(async_wait_status.get());
    // Prefer non cancelled errors to uncover real failures.
    if (TF_GetCode(async_wait_status.get()) != TF_OK &&
        (first_bad_status == nullptr ||
         TF_GetCode(first_bad_status.get()) == TF_CANCELLED)) {
      first_bad_status.reset(TF_NewStatus());