assertNotNull(is1, "Default input stream should not be null");
            assertNotNull(is2, "Input stream with sharing should not be null");
            assertNotNull(is3, "Input stream with full params should not be null");
            assertSame(mockInputStream, is1, "Should return expected input stream");
            assertSame(mockInputStream, is2, "Should return expected input stream");

from pydantic import BaseModel


def forwardref_method(input: "ForwardRefModel") -> "ForwardRefModel":
    return ForwardRefModel(x=input.x + 1)


class ForwardRefModel(BaseModel):

  @SafeVarargs
  public static <T extends @Nullable Object> Iterable<T> concat(Iterable<? extends T>... inputs) {
    return FluentIterable.concat(inputs);
  }

  /**
   * Combines multiple iterables into a single iterable. The returned iterable has an iterator that
   * traverses the elements of each iterable in {@code inputs}. The input iterators are not polled
   * until necessary.
   *

        this.taskName = taskName;
    }

    @Input
    public String getTaskName() {
        return taskName;
    }

    @Option(option = "tests", description = "Sets test class or method name to be included, '*' is supported.")
    public void setTestClass(String testClass) {
        this.testClass = testClass;
    }

    @Input
    public List<String> getExtraArgs() {
        return extraArgs;
    }

	unmarshaled     bool
	format          string
}

// IsEmpty - returns whether input serialization is empty or not.
func (input *InputSerialization) IsEmpty() bool {
	return !input.unmarshaled
}

// UnmarshalXML - decodes XML data.
func (input *InputSerialization) UnmarshalXML(d *xml.Decoder, start xml.StartElement) error {
	// Make subtype to avoid recursive UnmarshalXML().

@Experimental
public interface ModelTransformer {

    /**
     * Apply a transformation on the file model.
     *
     * @param model the input model
     * @param problems the problem collector to report any issues encountered during transformation
     * @return the transformed model, or the input model if no transformation is needed
     */
    @Nonnull
    Model transform(

    }

    @Test
    void readTrailerWireFormatShouldReadSixBytesSuccessfully() throws IOException {
        // Test successful reading of 6 bytes from the input stream.
        // The content of these bytes is not critical for this test, only that 6 bytes are read.
        byte[] data = { 0x01, 0x02, 0x03, 0x04, 0x05, 0x06 }; // Sample 6 bytes

                        "type": "object",
                        "properties": {
                            "ctx": {"title": "Context", "type": "object"},
                            "input": {"title": "Input"},
                            "loc": {
                                "title": "Location",
                                "type": "array",
                                "items": {

    @MethodSource("sizeWithScaleUnitTestData")
    void testSizeWithSelectedScaleUnit(long input, ScaleUnit unit, String expected) {
        FileSizeFormat format = new FileSizeFormat();
        if (unit == null) {
            assertEquals(expected, format.format(input));
        } else {
            assertEquals(expected, format.format(input, unit));
        }
    }

    @Test
    void testNegativeProgressedSize() {

        "Unable to find the placeholder node as input to enqueue in the TF "
        "graph.");
    return;
  }

  VLOG(1) << "Running the enqueue op";
  TF_Output input{placeholder_op, 0};
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ &input, /*input_values*/ &tensor, /*ninputs*/ 1,
                // output related parameters