* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

    *   Update sklearn imports for deprecated packages.
    *   Deprecate `Variable.count_up_to` and `tf.count_up_to` in favor of
        `Dataset.range`.
    *   Export `confusion_matrix` op as `tf.math.confusion_matrix` instead of
        `tf.train.confusion_matrix`.
    *   Add `tf.dtypes.` endpoint for every constant in dtypes.py. Moving
        endpoints in versions.py to corresponding endpoints in `tf.sysconfig.`

    assertEquals(int[].class, arrayOf(int.class).getRawType());
  }

  @SuppressWarnings("unused") // used by reflection
  private static class Holder<T> {
    List<T>[] matrix;

    void setList(List<T> list) {}
  }

  public void testWildcardCaptured_methodParameter_upperBound() throws Exception {
    TypeToken<Holder<?>> type = new TypeToken<Holder<?>>() {};

li').removeClass('active');
L614:            if (this.endDate == null) return;
L615:
L616:            this.calculateChosenLabel();
L617:        },
L618:
L619:        renderCalendar: function(side) {
L620:
L621:            //
L622:            // Build the matrix of dates that will populate the calendar
L623:            //
L624:
L625:            var calendar = side == 'left' ? this.leftCalendar : this.rightCalendar;
L626:            var month = calendar.month.month();
L627:            var year = calendar.month.year();
...

					<groupId>org.opensearch.plugin</groupId>
					<artifactId>mapper-extras-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>aggs-matrix-stats-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>rank-eval-client</artifactId>
				</exclusion>
				<exclusion>

    EXPECT_NE(TF_OK, TF_GetCode(status));
  } else {
    TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
    EXPECT_NE(TF_OK, TF_GetCode(status));
    EXPECT_EQ(nullptr, t);
    const char* msg = "Matrix size-incompatible: In[0]: [2,2], In[1]: [3,2]";
    EXPECT_TRUE(strstr(TF_Message(status), msg) != nullptr)
        << TF_Message(status);
    // Since error is not cleared, the following copy with correct device will

etc
ets
fins
fora
gairebé
ha
han
has
havia
he
hem
heu
hi 
ho
i
igual
iguals
ja
l'hi
la
les
li
li'n
llavors
m'he
ma
mal
malgrat
mateix
mateixa
mateixes
mateixos
me
mentre
més
meu
meus
meva
meves
molt
molta
moltes
molts
mon
mons
n'he
n'hi
ne
ni
no
nogensmenys
només

### Workloads that saturate nodes with pods may see pods that fail due to node admission

jetzt

// jewelry : 2015-03-05 Binky Moon, LLC
jewelry

// jio : 2015-04-02 Reliance Industries Limited
jio

// jll : 2015-04-02 Jones Lang LaSalle Incorporated
jll

// jmp : 2015-03-26 Matrix IP LLC
jmp

// jnj : 2015-06-18 Johnson & Johnson Services, Inc.
jnj

// joburg : 2014-03-24 ZA Central Registry NPC trading as ZA Central Registry
joburg

* 🔧 Update MkDocs for other languages. PR [#9734](https://github.com/tiangolo/fastapi/pull/9734) by [@tiangolo](https://github.com/tiangolo).
* 👷 Refactor Docs CI, run in multiple workers with a dynamic matrix to optimize speed. PR [#9732](https://github.com/tiangolo/fastapi/pull/9732) by [@tiangolo](https://github.com/tiangolo).