func commitID() string {
	// git log --format="%H" -n1
	var (
		commit []byte
		err    error
	)
	cmdName := "git"
	cmdArgs := []string{"log", "--format=%H", "-n1"}
	if commit, err = exec.Command(cmdName, cmdArgs...).Output(); err != nil {
		fmt.Fprintln(os.Stderr, "Error generating git commit-id: ", err)
		os.Exit(1)
	}

	return strings.TrimSpace(string(commit))
}

func commitTime() time.Time {
	// git log --format=%cD -n1

	for _, test := range lexTests {
		input := NewInput(test.name)
		input.Push(NewTokenizer(test.name, strings.NewReader(test.input), nil))
		result := drain(input)
		if result != test.output {
			t.Errorf("%s: got %q expected %q", test.name, result, test.output)
		}
	}
}

// lines joins the arguments together as complete lines.
func lines(a ...string) string {
	return strings.Join(a, "\n") + "\n"
}

package org.codelibs.curl.io;

import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
import java.util.logging.Logger;

import org.apache.commons.io.output.DeferredFileOutputStream;

/**
 * ContentOutputStream is a custom output stream that extends DeferredFileOutputStream.
 * It writes data to a temporary file once the data size exceeds a specified threshold.
 *

@REM Optional ENV vars
@REM   MVNW_REPOURL - repo url base for downloading maven distribution
@REM   MVNW_USERNAME/MVNW_PASSWORD - user and password for downloading maven
@REM   MVNW_VERBOSE - true: enable verbose log; others: silence the output
@REM ----------------------------------------------------------------------------

@IF "%__MVNW_ARG0_NAME__%"=="" (SET __MVNW_ARG0_NAME__=%~nx0)
@SET __MVNW_CMD__=
@SET __MVNW_ERROR__=

        help.execute(context);

        // Verify that logger.info was called multiple times
        // We can't easily verify the exact content without capturing the logger output,
        // but we can verify that the method executes without errors
        Mockito.verify(context.logger, Mockito.atLeastOnce()).info(Mockito.anyString());
    }

    @Test

  }

  VLOG(1) << "Running the dequeue op";
  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,

		{Bucket: "test-getobjectinfo", Name: "Asia/empty-dir/", ContentType: "application/octet-stream", IsDir: true},
	}
	testCases := []struct {
		bucketName string
		objectName string

		// Expected output of GetObjectInfo.
		result ObjectInfo
		err    error
		// Flag indicating whether the test is expected to pass or not.
		shouldPass bool
	}{
		// Test cases with invalid bucket names ( Test number 1-4 ).

        assertEquals(dataRemaining, response.getDataRemaining());

        // Verify data was copied to output buffer
        byte[] copiedData = Arrays.copyOfRange(outputBuffer, 0, dataLength);
        assertArrayEquals(testData, copiedData);
    }

    @Test
    @DisplayName("Should throw exception when output buffer too small")
    void testReadBytesWireFormatBufferTooSmall() throws Exception {
        // Given

      ListenableFuture<V> future) {
    if (future.isDone()) {
      return future;
    }
    NonCancellationPropagatingFuture<V> output = new NonCancellationPropagatingFuture<>(future);
    future.addListener(output, directExecutor());
    return output;
  }

  /** A wrapped future that does not propagate cancellation to its delegate. */

{* ../../docs_src/body_nested_models/tutorial003_py310.py hl[12] *}

With this, even if you receive a request with duplicate data, it will be converted to a set of unique items.

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

And it will be annotated / documented accordingly too.

## Nested Models { #nested-models }

Each attribute of a Pydantic model has a type.