* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.

* **Deep Learning**: Esse é um subcampo do Machine Learning, então, o mesmo se aplica. A diferença é que não há apenas uma grande planilha com números para multiplicar, mas um grande conjunto delas, e em muitos casos, você utiliza um processador especial para construir e/ou usar esses modelos.

          apiToken: ${{ secrets.CLOUDFLARE_API_TOKEN }}
          accountId: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }}
          command: pages deploy ./site --project-name=${{ env.PROJECT_NAME }} --branch=${{ env.BRANCH }}
      - name: Comment Deploy
        run: python ./scripts/deploy_docs_status.py
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          DEPLOY_URL: ${{ steps.deploy.outputs.deployment-url }}

        // Act + Assert
        assertThrows(NullPointerException.class, () -> SmbCopyUtil.openCopyTargetFile(dest, SmbConstants.ATTR_NORMAL, false));
    }

    // --- Tests for copyFile exception handling branch ---

    @Nested
    class CopyFileExceptions {

        private SmbTreeHandleImpl newTreeHandle(boolean isSmb2) throws Exception {
            SmbTreeHandleImpl th = mock(SmbTreeHandleImpl.class);

      if (type == null) {
        Type[] bounds = var.getBounds();
        if (bounds.length == 0) {
          return var;
        }
        Type[] resolvedBounds = new TypeResolver(forDependants).resolveTypes(bounds);
        /*
         * We'd like to simply create our own TypeVariable with the newly resolved bounds. There's

      HashFunction hasher = Hashing.goodFastHash(i);
      assertTrue(hasher.bits() >= i);
      HashTestUtils.assertInvariants(hasher);
    }
  }

  // goodFastHash(32) uses Murmur3_32. Use the same epsilon bounds.
  public void testGoodFastHash32() {
    HashTestUtils.check2BitAvalanche(Hashing.goodFastHash(32), 250, 0.20);
    HashTestUtils.checkAvalanche(Hashing.goodFastHash(32), 250, 0.17);

1.  API changes require discussion, use cases, etc. Code comes later.
2.  Pull requests are great for small fixes for bugs, documentation, etc.
3.  Pull requests are not merged directly into the master branch.
4.  Code contributions require signing a Google CLA.

API changes
-----------

We make changes to Guava's public [APIs][], including adding new APIs, very

        buffer.putInt(4, data.length);
        result = NtlmMessage.readSecurityBuffer(data, 0);
        assertEquals(0, result.length, "Should return empty array if offset is out of bounds but length is zero.");

        // Test with offset pointing outside array bounds (should throw ArrayIndexOutOfBoundsException)
        buffer.putShort(0, (short) 1);
        buffer.putShort(2, (short) 1);
        buffer.putInt(4, data.length + 1); // Invalid offset

    outputs:
      docs: ${{ steps.filter.outputs.docs }}
    steps:
    - uses: actions/checkout@v5
    # For pull requests it's not necessary to checkout the code but for the main branch it is
    - uses: dorny/paths-filter@v3
      id: filter
      with:
        filters: |
          docs:
            - README.md
            - docs/**
            - docs_src/**

        }
    }

    /**
     * Validates array bounds for safe copying
     *
     * @param src source array
     * @param srcOffset source offset
     * @param dst destination array
     * @param dstOffset destination offset
     * @param length copy length
     * @throws IllegalArgumentException if bounds are invalid
     */

          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"

  # https://github.com/marketplace/actions/alls-green#why
  test-redistribute-alls-green:  # This job does nothing and is only used for the branch protection
    if: always()
    needs:
      - test-redistribute
    runs-on: ubuntu-latest
    steps:
      - name: Decide whether the needed jobs succeeded or failed
        uses: re-actors/alls-green@release/v1