%array_key, %tensor = "tf.IfrtLoadVariable"(%0) <{used_by_host = true}> : (tensor<!tf_type.resource<tensor<3x1xf32>>>) -> (tensor<!tf_type.string>, tensor<3x1xf32>)
    %1 = "tf.MatMul"(%arg0, %tensor) : (tensor<1x3xf32>, tensor<3x1xf32>) -> tensor<1x1xf32>

            logger.debug("Add analyzer settings. {} key: {} value: {}", arraySettingsIndexName, key, value);
        }

        final Map<String, Object> source = new HashMap<>();
        source.put(FieldNames.ARRAY_KEY, key);
        source.put(FieldNames.ARRAY_VALUE, value);
        source.put(FieldNames.TIMESTAMP, DateTimeFormatter.ISO_INSTANT.format(ZonedDateTime.now()));

inline bool ArrayEq(const T& lhs, const U& rhs) { return lhs == rhs; }

// This overload is used when k >= 1.
template <typename T, typename U, size_t N>
inline bool ArrayEq(const T(&lhs)[N], const U(&rhs)[N]) {
  return internal::ArrayEq(lhs, N, rhs);
}

// This helper reduces code bloat.  If we instead put its logic inside
// the previous ArrayEq() function, arrays with different sizes would

inline bool ArrayEq(const T& lhs, const U& rhs) { return lhs == rhs; }

// This overload is used when k >= 1.
template <typename T, typename U, size_t N>
inline bool ArrayEq(const T(&lhs)[N], const U(&rhs)[N]) {
  return internal::ArrayEq(lhs, N, rhs);
}

// This helper reduces code bloat.  If we instead put its logic inside
// the previous ArrayEq() function, arrays with different sizes would

    public static final String ROLES = "roles";
    public static final String FIELDS = "fields";
    public static final String LANGUAGES = "languages";

    public static final String ARRAY_KEY = "key";
    public static final String ARRAY_VALUE = "value";

    public static final String ANALYZER_SETTINGS_TYPE = "settingsType";
    public static final String ANALYZER_SETTINGS_FIELD_NAME = "fieldName";

    The `tf.IfrtCall` kernel uses the output $array_key.
    Other ops executed by TFRT may make use of $tensor_future.
  }];

  let arguments = (ins
    Arg<TF_Tensor, [{The variable tensor to be loaded}], []>:$variable,
    DefaultValuedAttr<BoolAttr, "false">:$used_by_host
  );

  let results = (outs
    TF_StrTensor:$array_key,
    TF_Tensor: $tensor_future
  );

        }

        final Map<String, Object> source = new HashMap<>();
        source.put(FieldNames.ARRAY_KEY, ELEVATE_WORD_SETTINGD_KEY);
        source.put(FieldNames.ARRAY_VALUE, elevateWord.getElevateWord());
        source.put(ELEVATE_WORD_BOOST, elevateWord.getBoost());

  }];

  let arguments = (ins
    TF_Tensor:$variable,
    DefaultValuedAttr<BoolAttr, "false">:$used_by_host
  );

  let results = (outs
    TF_Tensor:$array_key,
    MlrtFutureType: $tensor_future
  );
}

    The `tf.IfrtCall` kernel uses the output $array_key.
    Other ops executed by TFRT may make use of $tensor_future.
  }];

  let arguments = (ins
    TFTensorType:$variable,
    DefaultValuedAttr<BoolAttr, "false">:$used_by_host
  );

  let results = (outs
    TFTensorType:$array_key,
    MlrtFutureType: $tensor_future
  );
}

ResizableArray.class package scala.collection.mutable; public abstract interface ResizableArray extends scala.collection.IndexedSeqOptimized, IndexedSeq { public abstract int initialSize(); public abstract Object[] array(); public abstract void array_$eq(Object[]); public abstract int size0(); public abstract void size0_$eq(int); public abstract int length(); } scala/collection/mutable/MapLike.class package scala.collection.mutable; public abstract interface MapLike extends scala.Cloneable, scal...