The risk of loading untrusted checkpoints depends on the code or graph that you
are working with. When loading untrusted checkpoints, the values of the traced
variables from your model are also going to be untrusted. That means that if
your code interacts with the filesystem, network, etc. and uses checkpointed
variables as part of those interactions (ex: using a string variable to build a

          startDraining(true);
          continue DRAINING;
        } else if (result.isUnderflow()) {
          // If encoder underflows, it means either:
          // a) the final flush() succeeded; next drain (then done)
          // b) we encoded all of the input; next flush
          // c) we ran of out input to encode; next read more input

    putEdge(N1, N1);
    assertThat(graph.outDegree(N1)).isEqualTo(1);
    putEdge(N1, N2);
    assertThat(graph.outDegree(N1)).isEqualTo(2);
  }

  // Stable order tests

  // Note: Stable order means that the ordering doesn't change between iterations and versions.
  // Ideally, the ordering in test should never be updated.
  @Test
  public void stableIncidentEdgeOrder_edges_returnsInStableOrder() {

    }

    /**
     * Check if path is whitelisted
     */
    private boolean isWhitelisted(String path) {
        if (whitelistedPaths.isEmpty()) {
            return true; // No whitelist means all paths allowed
        }

        String lowerPath = path.toLowerCase();
        for (String whitelisted : whitelistedPaths) {
            if (lowerPath.startsWith(whitelisted) || lowerPath.equals(whitelisted)) {

> NOTE: Server side replication is supported for idempotent versions on delete marked objects.

### Motivation

    "groups" : [ {
      "testProject" : "largeJavaMultiProject",
      "comment" : "We only test the multi-project here since for the monolithic project we would have no cache hits. This would mean we actually would test incremental compilation.",
      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    } ]
  }, {

 * side, this yields nine basic types of ranges, enumerated below. (Notation: a square bracket
 * ({@code [ ]}) indicates that the range is closed on that side; a parenthesis ({@code ( )}) means
 * it is either open or unbounded. The construct {@code {x | statement}} is read "the set of all
 * <i>x</i> such that <i>statement</i>.")
 *
 * <blockquote>
 *
 * <table>
 * <caption>Range Types</caption>

        table.put(row, column, cell);
      } else {
        oldCell.merge(value, merger);
      }
    }

    /*
     * While the current implementation returns `this`, that's not something we mean to guarantee.
     * Anyway, the purpose of this method is to implement a BinaryOperator combiner for a Collector,
     * so its return value will get used naturally.
     */

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

   * Generation, described in Knuth's "The Art of Computer Programming", Volume 4, Chapter 7,
   * Section 7.2.1.2. The iteration order follows the lexicographical order. This means that the
   * first permutation will be in ascending order, and the last will be in descending order.
   *
   * <p>Duplicate elements are considered equal. For example, the list [1, 1] will have only one