}

  @CollectionFeature.Require(SUPPORTS_ITERATOR_REMOVE)
  @CollectionSize.Require(ONE)
  public void testValuesIteratorRemove() {
    Iterator<V> valuesItr = multimap().values().iterator();
    valuesItr.next();
    valuesItr.remove();
    assertTrue(multimap().isEmpty());
  }

      return !valueCollection.isEmpty() && get(key).addAll(valueCollection);
    } else {
      Iterator<? extends V> valueItr = values.iterator();
      return valueItr.hasNext() && Iterators.addAll(get(key), valueItr);
    }
  }

  @CanIgnoreReturnValue
  @Override
  public boolean putAll(Multimap<? extends K, ? extends V> multimap) {
    boolean changed = false;

	const (
		patchCommon = `overlays:
- kind: Deployment
  name: istio-citadel
  patches:
  - path: `
		valueStr = `    value: `
	)

	ret := patchCommon
	ret += fmt.Sprintf("%s\n", path)
	if value != "" {
		ret += fmt.Sprintf("%s%s\n", valueStr, value)
	}
	return ret
}

// errToString returns the string representation of err and the empty string if
// err is nil.

      // Convert values.
      std::vector<Eigen::half> new_values;
      const DenseFPElementsAttr value_attr =
          mlir::cast<DenseFPElementsAttr>(op.getValue());
      new_values.reserve(value_attr.getNumElements());

      for (const float value : value_attr.getValues<float>()) {
        new_values.push_back(Eigen::half(
            std::min(std::max(value, kMinFloat16Value), kMaxFloat16Value)));

        cast<mlir::stablehlo::ConstantOp>(op.getOperand().getDefiningOp());

    const auto value_attr =
        mlir::cast<DenseFPElementsAttr>(const_op.getValue());
    const ArrayRef<int64_t> original_shape =
        value_attr.getShapedType().getShape();

    const SmallVector<float> original_values =
        llvm::to_vector(value_attr.getValues<float>());

    // Fold the constant value by transposing the values according to the

      return !valueCollection.isEmpty() && get(key).addAll(valueCollection);
    } else {
      Iterator<? extends V> valueItr = values.iterator();
      return valueItr.hasNext() && Iterators.addAll(get(key), valueItr);
    }
  }

  @CanIgnoreReturnValue
  @Override
  public boolean putAll(Multimap<? extends K, ? extends V> multimap) {
    boolean changed = false;

        1;
    mark_for_compilation_flags->tf_xla_ops_to_cluster = "FUSIBLE";
    return true;
  }

  absl::string_view value_sv(value);
  if (!absl::ConsumePrefix(&value_sv, "single-gpu(") ||
      !absl::ConsumeSuffix(&value_sv, ")") ||
      !absl::SimpleAtoi(value_sv, &opt_level)) {
    return false;
  }

  mark_for_compilation_flags->xla_auto_jit_flag.optimization_level_single_gpu =
      opt_level;

      // Cast values
      std::vector<Eigen::half> new_values;
      DenseFPElementsAttr value_attr =
          mlir::cast<DenseFPElementsAttr>(op.getValue());
      new_values.reserve(value_attr.getNumElements());

      constexpr float kMaxFloat16Value = 65504.f;
      constexpr float kMinFloat16Value = -65504.f;

      for (auto value : value_attr.template getValues<float>()) {
        new_values.push_back(Eigen::half(

    mlir::Builder b(expected_ty.getContext());
    Tensor tensor(dtype, TensorShape({static_cast<int64_t>(values.size())}));
    tensor.flat<T>().setValues(values);

    auto value_or = ConvertTensor(tensor, &b);
    TF_ASSERT_OK(value_or.status());
    auto attr = value_or.value();

    EXPECT_EQ(attr.getShapedType().getElementType(), expected_ty);

    Tensor out;
    TF_ASSERT_OK(ConvertToTensor(attr, &out));

        K key = (K) elements[i];

        Iterator<V> valueItr = valueIterators.get(key);
        if (valueItr == null) {
          valueIterators.put(key, valueItr = sampleValuesIterator());
        }
        entries[i] = mapEntry(key, valueItr.next());
      }
      return multimapGenerator.create((Object[]) entries).keys();
    }