if (object instanceof Multiset.Entry) {
        Entry<?> entry = (Entry<?>) object;
        Object element = entry.getElement();
        int entryCount = entry.getCount();
        if (entryCount != 0) {
          // Safe as long as we never add a new entry, which we won't.
          // (Presumably it can still throw CCE/NPE but only if the underlying Multiset does.)

      if (object instanceof Multiset.Entry) {
        Entry<?> entry = (Entry<?>) object;
        Object element = entry.getElement();
        int entryCount = entry.getCount();
        if (entryCount != 0) {
          // Safe as long as we never add a new entry, which we won't.
          // (Presumably it can still throw CCE/NPE but only if the underlying Multiset does.)

".go") if err != nil { log.Fatal(err) } if err := t.Execute(f, c); err != nil { log.Fatal(err) } if err := f.Close(); err != nil { log.Fatal(err) } log.Printf("Generating %s_fiat64.go...", c.Prefix) cmd := exec.Command("docker", "run", "--rm", "--entrypoint", "word_by_word_montgomery", "fiat-crypto:v0.0.9", "--lang", "Go", "--no-wide-int", "--cmovznz-by-mul", "--relax-primitive-carry-to-bitwidth", "32,64", "--internal-static", "--public-function-case", "camelCase", "--public-type-case", "camelCase",...

".go") if err != nil { log.Fatal(err) } if err := t.Execute(f, c); err != nil { log.Fatal(err) } if err := f.Close(); err != nil { log.Fatal(err) } log.Printf("Generating %s_fiat64.go...", c.Prefix) cmd := exec.Command("docker", "run", "--rm", "--entrypoint", "word_by_word_montgomery", "fiat-crypto:v0.0.9", "--lang", "Go", "--no-wide-int", "--cmovznz-by-mul", "--relax-primitive-carry-to-bitwidth", "32,64", "--internal-static", "--public-function-case", "camelCase", "--public-type-case", "camelCase",...

    accepts a `return_attention_scores` argument. When set to True, the layer
    returns the attention scores as an additional output argument.
*   Adds `tf.metrics.log_cosh` and `tf.metrics.logcosh` API entrypoints with the
    same implementation as their `tf.losses` equivalent.
*   For Keras model, the individual call of `Model.evaluate` uses no cached data
    for evaluation, while `Model.fit` uses cached data when `validation_data`