* Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
     * If error still persists then, apparently your CPU model does not support AVX instruction sets.
       * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).
   

			t.Logf("%#v %#v", v.h, fi.Sys())
			t.Errorf("i=%d: Typeflag: got %q, want %q", i, got, want)
		}
		if got, want := h2.Mode, v.h.Mode; got != want {
			t.Errorf("i=%d: Mode: got %o, want %o", i, got, want)
		}
		if got, want := fi.Mode(), v.fm; got != want {
			t.Errorf("i=%d: fi.Mode: got %o, want %o", i, got, want)
		}
		if got, want := h2.AccessTime, v.h.AccessTime; got != want {

            "spanNulls": false,
            "stacking": {
              "group": "A",
              "mode": "none"
            },
            "thresholdsStyle": {
              "mode": "off"
            }
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "green",
                "value": null

    public SmbRandomAccessFile(final String url, final String mode, final int sharing, final CIFSContext tc)
            throws SmbException, MalformedURLException {
        this(new SmbFile(url, tc), mode, sharing, true);
    }

    /**
     * Instantiate a random access file from a {@link SmbFile}
     *
     * @param file the SmbFile to access
     * @param mode the access mode ("r" for read-only, "rw" for read-write)

            "spanNulls": false,
            "stacking": {
              "group": "A",
              "mode": "none"
            },
            "thresholdsStyle": {
              "mode": "off"
            }
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "green",
                "value": null

 */
@Deprecated
public class DefaultJavaToolChain extends JavaToolchainImpl {
    public static final String KEY_JAVAHOME = JavaToolchainImpl.KEY_JAVAHOME;

    public DefaultJavaToolChain(ToolchainModel model, Logger logger) {
        super(model, logger);
    }

    @Override
    public String getJavaHome() {
        return super.getJavaHome();
    }

    @Override
    public void setJavaHome(String javaHome) {

 */

package jcifs.smb;

/**
 * Interface defining DOS error codes and their mappings to NT status codes.
 * Provides lookup tables for converting between DOS and NT error representations.
 */
public interface DosError {

    /**
     * Mapping table from DOS error codes to NT status codes.
     * Each entry contains a pair of [DOS error code, NT status code].
     */

"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied.  See the License for the
specific language governing permissions and limitations
under the License.
-->

<model xmlns="http://modello.codehaus.org/MODELLO/1.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://modello.codehaus.org/MODELLO/1.0.0 http://modello.codehaus.org/xsd/modello-1.0.0.xsd"

sets, which is *16* 64 = 1024* drives in total.

- *If total number of nodes are of odd number then GCD algorithm provides affinity towards odd number erasure sets to provide for uniform distribution across nodes*. This is to ensure that same number of drives are pariticipating in any erasure set. For example if you have 2 nodes with 180 drives then GCD is 15 but this would lead to uneven distribution, one of the nodes would participate more drives. To avoid this the affinity is given towards...

import org.apache.maven.execution.MavenSession;
import org.apache.maven.model.Repository;
import org.apache.maven.model.building.ModelBuildingException;
import org.apache.maven.model.building.ModelBuildingRequest;
import org.apache.maven.model.building.ModelSource;
import org.apache.maven.model.building.UrlModelSource;
import org.apache.maven.plugin.LegacySupport;
import org.apache.maven.profiles.ProfileManager;