return Arrays.copyOfRange(source, from, to, (Class<? extends T[]>) arrayOfType.getClass());
  }

  /**
   * Configures the given map maker to use weak keys, if possible; does nothing otherwise (i.e., in
   * GWT). This is sometimes acceptable, when only server-side code could generate enough volume
   * that reclamation becomes important.
   */

    int64_t target_id = target_tensor_ids[i];
    if (sources_set.find(target_id) != sources_set.end()) {
      auto tensor = targets[i];
      sources_that_are_targets.insert(
          std::make_pair(target_id, TapeTensor(tensor)));
    }
  }

  TF_RETURN_IF_ERROR(GradientTape::ComputeGradient(
      vspace, target_tensor_ids, source_tensor_ids, sources_that_are_targets,

      // (such as uniqueness of the names of nodes) we run with other functions
      // that add a node to the graph (like TF_FinishOperation).
      if (!g->name_map.insert(std::make_pair(n->name(), n)).second) {
        status->status = tensorflow::errors::Internal(
            "BUG: The API allowed construction of a graph with duplicate node "
            "names (",
            n->name(),

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maif

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makeup

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// https://www.iana.org/domains/root/db/man.html
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madethis.site
madrid
maebashi.gunma.jp
mafelo.net
magazine.aero
maibara.shiga.jp
maif
mail-box.ne.jp
mail.pl
main.jp
maintenance.aero
maison
maizuru.kyoto.jp
makeup
makinohara.shizuoka.jp
makurazaki.kagoshima.jp
malatvuopmi.no
malbork.pl
malopolska.pl
malselv.no
malvik.no
mamurogawa.yamagata.jp
man
management
manaus.br
mandal.no

    *   Add `include_preprocessing` argument to `MobileNetV3` architectures to
        control the inclusion of `Rescaling` layer in the model.
    *   Add optional argument (`force`) to `make_(train|test|predict)_funtion`
        methods to skip the cached function and generate a new one. This is
        useful to regenerate in a single call the compiled training function