in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs...

package jcifs.smb.compression;

import jcifs.CIFSException;

/**
 * Interface for SMB3 compression services.
 *
 * Provides compression and decompression functionality for SMB3 protocol
 * data transfers with support for multiple compression algorithms.
 */
public interface CompressionService {

    /**
     * Compression algorithm constants matching SMB3 specification.
     */
    public static final int COMPRESSION_NONE = 0x0;

      return reader.read() == -1;
    } catch (Throwable e) {
      throw closer.rethrow(e);
    } finally {
      closer.close();
    }
  }

  /**
   * Concatenates multiple {@link CharSource} instances into a single source. Streams returned from
   * the source will contain the concatenated data from the streams of the underlying sources.
   *

      return reader.read() == -1;
    } catch (Throwable e) {
      throw closer.rethrow(e);
    } finally {
      closer.close();
    }
  }

  /**
   * Concatenates multiple {@link CharSource} instances into a single source. Streams returned from
   * the source will contain the concatenated data from the streams of the underlying sources.
   *

        assertEquals("test_stackTrace", stackTrace[0].getMethodName());
        assertEquals(this.getClass().getName(), stackTrace[0].getClassName());
    }

    public void test_multipleCatches() {
        // Test with multiple catch blocks
        boolean caughtSpecific = false;
        boolean caughtRuntime = false;

        try {
            throw new DictionaryExpiredException();
        } catch (DictionaryExpiredException e) {

            assertEquals("User is not found: testuser", e.getCause().getMessage());
        }
    }

    public void test_multipleInstances() {
        // Test that multiple instances are independent
        FessUserNotFoundException exception1 = new FessUserNotFoundException("user1");
        FessUserNotFoundException exception2 = new FessUserNotFoundException("user2");

        ApiResult result = errorResponse.result();

        assertNotNull(result);
        // Cannot directly access protected fields, just verify the result is not null
    }

    // Test multiple configurations
    public void test_multipleConfigurations() {
        // Test with exception included = true
        ComponentUtil.setFessConfig(new FessConfig.SimpleImpl() {

        assertTrue(result.contains(expectedHashCode));
    }

    // Test toString() format consistency
    public void test_toString_formatConsistency() {
        // Create multiple instances and verify consistent format
        FessUserTimeZoneProcessProvider provider1 = new FessUserTimeZoneProcessProvider();
        FessUserTimeZoneProcessProvider provider2 = new FessUserTimeZoneProcessProvider();

import jakarta.annotation.PostConstruct;

/**
 * Helper class for managing duplicate host configurations in the Fess search system.
 * This class handles URL conversion based on duplicate host rules, allowing multiple
 * hostnames or URLs to be treated as equivalent for crawling and indexing purposes.
 * It maintains a list of DuplicateHost rules and applies them to URLs.
 *
 */
public class DuplicateHostHelper {

///

## Duplicate headers { #duplicate-headers }

It is possible to receive duplicate headers. That means, the same header with multiple values.

You can define those cases using a list in the type declaration.

You will receive all the values from the duplicate header as a Python `list`.