@Nonnull
    default Map<String, String> getEffectiveProperties() {
        return getEffectiveProperties(null);
    }

    /**
     * Each invocation computes a new map of effective properties. To be used in interpolation.
     * <p>
     * Effective properties are computed from system, user and optionally project properties, layered with
     * defined precedence onto each other to achieve proper precedence. Precedence is defined as:

            place during the execution of a plugin. This field must be managed
            by the generated parser and formatter classes in order to allow it
            to survive model interpolation.
          </description>
          <type>String</type>
        </field>
      </fields>
      <codeSegments>
        <codeSegment>
            <version>4.0.0+</version>
            <code>

import org.apache.maven.api.annotations.Nullable;

/**
 * The Interpolator service provides methods for variable substitution in strings and maps.
 * It allows for the replacement of placeholders (e.g., ${variable}) with their corresponding values.
 *
 * @since 4.0.0
 */
@Experimental
public interface Interpolator extends Service {

    /**

            0, 1.0,
            1, 1.5,
            2, 2.0,
            8, 5.0,
            9, POSITIVE_INFINITY, // interpolating between 5.0 and POSITIVE_INFINITY
            10, POSITIVE_INFINITY);
  }

  public void testScale_index_compute_doubleCollection_positiveInfinity() {
    // interpolating between 5.0 and POSITIVE_INFINITY
    assertThat(Quantiles.scale(10).index(9).compute(ONE_TO_FIVE_AND_POSITIVE_INFINITY))

            0, 1.0,
            1, 1.5,
            2, 2.0,
            8, 5.0,
            9, POSITIVE_INFINITY, // interpolating between 5.0 and POSITIVE_INFINITY
            10, POSITIVE_INFINITY);
  }

  public void testScale_index_compute_doubleCollection_positiveInfinity() {
    // interpolating between 5.0 and POSITIVE_INFINITY
    assertThat(Quantiles.scale(10).index(9).compute(ONE_TO_FIVE_AND_POSITIVE_INFINITY))

*   Added warmup capabilities to `tf.keras.optimizers.schedules.CosineDecay` learning rate scheduler. You can now specify an initial and target learning rate, and our scheduler will perform a linear interpolation between the two after which it will begin a decay phase.

                log.warn("""
                        Default credentials (jcifs.smb.client.username/password)\
                         not specified. SMB signing may not work propertly.\
                          Skipping DC interrogation.""");
            } else {
                try (SmbSessionInternal ssn = trans.getSmbSession(tf.withDefaultCredentials()).unwrap(SmbSessionInternal.class)) {
                    ssn.treeConnectLogon();
                }

                        Default credentials (jcifs.smb1.smb1.client.username/password)\
                         not specified. SMB signing may not work propertly.\
                          Skipping DC interrogation.""");
            }
        } else {
            final SmbSession ssn = trans.getSmbSession(NtlmPasswordAuthentication.DEFAULT);
            ssn.getSmbTree(LOGON_SHARE, null).treeConnect(null, null);
        }