ty = self._get_inferred_type(node.args[0], ty)
        if ty == TFRTypes.TF_TENSOR_SHAPE_LIST:
          len_value = self._ssa_name('len')
          self._emit_with_loc(
              '\n{} = shape.rank {} : !shape.shape -> !shape.size'.format(
                  len_value, arg), node)
          size_value = self._ssa_name('len_size')
          self._emit_with_loc(

  devices_.UnionWith(other->devices_);

  DCHECK(!(resource_op_device_.has_value() &&
           other->resource_op_device_.has_value()) ||
         *resource_op_device_ == *other->resource_op_device_)
      << "AreDevicesCompatible should have returned false otherwise!";

  if (!resource_op_device_.has_value()) {
    resource_op_device_ = other->resource_op_device_;
  }

	zero := MustParse("0")
	for _, testValue := range testValues {
		value := MustParse(testValue)
		v1 := value.DeepCopy()
		// ensure non-zero + zero = non-zero (suffix preserved)
		v1.Add(zero)
		// ensure zero + non-zero = non-zero (suffix preserved)
		v2 := zero.DeepCopy()
		v2.Add(value)

		if v1.String() != testValue {
			t.Errorf("Expected %v, actual %v", testValue, v1.String())
			continue
		}

  string ToString() const override {
    return must_have_value().has_value()
               ? absl::StrCat(tensor_id_.ToString(), "=", *must_have_value_)
               : tensor_id_.ToString();
  }

  Kind kind() const override { return Kind::kIntSymbol; }
  absl::Span<Predicate* const> GetOperands() const override { return {}; }

  // If `must_have_value().has_value()` is true, then this IntSymbolPredicate

      mlir::Value new_value = input.getOutput();

      if (mlir::isa<TF::TPUAnnotateTensorsWithDynamicShapeOp>(
              result.getDefiningOp())) {
        TF::TPUAnnotateTensorsWithDynamicShapeOp annotate_op =
            builder.create<TF::TPUAnnotateTensorsWithDynamicShapeOp>(
                op->getLoc(), result.getType(), new_value,
                result.getDefiningOp()->getAttrs());

	fakeMetricsClient.AddReactor("list", "pods", func(action core.Action) (handled bool, ret runtime.Object, err error) {
		if tc.resource != nil {
			metrics := &metricsapi.PodMetricsList{}
			for i, resValue := range tc.resource.levels {
				podName := fmt.Sprintf("%s-%d", podNamePrefix, i)
				if len(tc.resource.podNames) > i {
					podName = tc.resource.podNames[i]
				}

                try {
                    if (StringUtil.isBlank(prev)) {
                        final Map<String, Map<String, String>> result =
                                mapper.readValue(line, new TypeReference<Map<String, Map<String, String>>>() {
                                });
                        if (result.containsKey("index") || result.containsKey("update")) {
                            return line;

  const char* value() const {
    return value_.c_str();
  }

  // Sets a new value, overriding the one supplied in the constructor.
  void SetValue(const std::string& new_value) {
    value_ = new_value;
  }

 private:
  // The key supplied by the user.
  std::string key_;
  // The value supplied by the user.
  std::string value_;
};

  const char* value() const {
    return value_.c_str();
  }

  // Sets a new value, overriding the one supplied in the constructor.
  void SetValue(const std::string& new_value) {
    value_ = new_value;
  }

 private:
  // The key supplied by the user.
  std::string key_;
  // The value supplied by the user.
  std::string value_;
};

  if (variant_ty.getSubtypes().size() == 1) {
    return true;
  }
  // If subtype info is not available, check if the value is used by any of
  // the following TensorList operations.
  if (!value.has_value()) {
    return false;
  }
  for (const mlir::OpOperand &use : value.value().getUses()) {
    mlir::Operation *op = use.getOwner();
    if (llvm::isa<TF::TensorListGetItemOp>(op) ||