/*inputs=*/{x.get()},
                      /*outputs=*/{},
                      /*use_function=*/UseFunction());
  ASSERT_EQ(errors::OK, s.code()) << s.message();
}

// Checks that inputs[0] is a matrix with shape 2x4.
Status TestTensorShape2x4(AbstractContext* ctx,
                          absl::Span<AbstractTensorHandle* const> inputs,
                          absl::Span<AbstractTensorHandle*> outputs) {

-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

/**
 * Evaluator for plugin parameters expressions. Content surrounded by <code>${</code> and <code>}</code> is evaluated.
 * Recognized values are:
 * <table border="1">
 * <caption>Expression matrix</caption>
 * <tr><th>expression</th>                     <th></th>               <th>evaluation result</th></tr>
 * <tr><td><code>session.*</code></td>         <td></td>               <td></td></tr>

/**
 * Evaluator for plugin parameters expressions. Content surrounded by <code>${</code> and <code>}</code> is evaluated.
 * Recognized values are:
 * <table border="1">
 * <caption>Expression matrix</caption>
 * <tr><th>expression</th>                     <th></th>               <th>evaluation result</th></tr>
 * <tr><td><code>session</code></td>           <td></td>               <td>the actual {@link MavenSession}</td></tr>

* **Maschinelles Lernen**: Normalerweise sind viele „Matrix“- und „Vektor“-Multiplikationen erforderlich. Stellen Sie sich eine riesige Tabelle mit Zahlen vor, in der Sie alle Zahlen gleichzeitig multiplizieren.

    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<?>>() {};

* **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<?>>() {};

					<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>