permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      matrix:
        pyver: ['3.10']
    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \

    branches: [master]
  schedule:
    - cron: '0 11 * * 4'

jobs:
  analyze:
    name: Analyze
    runs-on: ubuntu-latest

    strategy:
      fail-fast: false
      matrix:
        # Override automatic language detection by changing the below list
        # Supported options are ['csharp', 'cpp', 'go', 'java', 'javascript', 'python']
        language: ['java']
        # Learn more...

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      fail-fast: false
      matrix:
        pyver: ['3.9', '3.10', '3.11', '3.12']
    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \

  /* Want to test simple MatMul example with abstract tensors:
    [[1,2],   *  [[5,6],   =   [[19,22],
     [3,4]]       [7,8]]        [43,50]]
  */

  // Build 1st Matrix.
  int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
  int num_dims = sizeof(dims) / sizeof(dims[0]);

  float vals1[] = {1.0f, 2.0f, 3.0f, 4.0f};

                      /*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<?>>() {};