@SuppressWarnings("unused") // used by reflection
    List<N> counts;
  }

  public void testWildcardCaptured_typeVariableDeclaresTypeBound_wildcardHasNoExplicitUpperBound()
      throws Exception {
    TypeToken<Counter<?>> type = new TypeToken<Counter<?>>() {};
    TypeToken<?> fieldType =
        type.resolveType(Counter.class.getDeclaredField("counts").getGenericType());

    @SuppressWarnings("unused") // used by reflection
    List<N> counts;
  }

  public void testWildcardCaptured_typeVariableDeclaresTypeBound_wildcardHasNoExplicitUpperBound()
      throws Exception {
    TypeToken<Counter<?>> type = new TypeToken<Counter<?>>() {};
    TypeToken<?> fieldType =
        type.resolveType(Counter.class.getDeclaredField("counts").getGenericType());

//
// If index is out of bounds, an error code will be set in the status object,
// and -1 will be returned.
TF_CAPI_EXPORT extern int64_t TF_DeviceListMemoryBytes(
    const TF_DeviceList* list, int index, TF_Status* status);

// Retrieve the incarnation number of a given device.
//
// If index is out of bounds, an error code will be set in the status object,
// and 0 will be returned.

	// excludes these objects (and hence only goes up between GCs).
	//
	// To reduce contention, this is updated only when obtaining a span
	// from an mcentral and at this point it counts all of the unallocated
	// slots in that span (which will be allocated before that mcache
	// obtains another span from that mcentral). Hence, it slightly
	// overestimates the "true" live heap size. It's better to overestimate

  result is true.

This matches the behavior of If and While for determining if a tensor counts
as true/false for a branch condition.
  }];

  let arguments = (ins
    TF_Tensor:$input
  );

  let results = (outs
    I1Tensor:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;

  let hasCanonicalizer = 1;

	//   requires keeping a surprising amount of state on the stack, and we have
	//   to run the cgo symbolizer twice—once to count frames, and a second to
	//   print them—since we can't retain the strings it returns.
	//
	// Instead, we print the outer frames, and if we reach that limit, we clone
	// the unwinder, count the remaining frames, and then skip forward and
	// finish printing from the clone. This makes two passes over the outer part

	add(t, dc.podStore, pod)
	dc.sync(ctx, pdbName)
	ps.VerifyPdbStatus(t, pdbName, 0, 1, 3, 1, map[string]metav1.Time{})
}

// Verify that available/expected counts go up as we add pods, then verify that
// available count goes down when we make a pod unavailable.
func TestUnavailable(t *testing.T) {
	_, ctx := ktesting.NewTestContext(t)
	dc, ps := newFakeDisruptionController(ctx)

   *
   * <p>Counts 2 per supplementary character, such as for {@link #whitespace}().{@link #negate}().
   */
  public int countIn(CharSequence sequence) {
    int count = 0;
    for (int i = 0; i < sequence.length(); i++) {
      if (matches(sequence.charAt(i))) {
        count++;
      }
    }
    return count;
  }

  /**

// floating point and quantized space. It is designed to reproduce TF's
// implementation, mirroring the previous XLA implementation.
//
// 1. Computing proper quantized bounds. This involves nudging the input bounds.
// 2. Converting the input bounds to quantized space, rounding values.
// 3. Convert back into floating point space.
class ConvertFakeQuantWithMinMaxVarsOp : public RewritePattern {
 public:

   *
   * <p>Counts 2 per supplementary character, such as for {@link #whitespace}().{@link #negate}().
   */
  public int countIn(CharSequence sequence) {
    int count = 0;
    for (int i = 0; i < sequence.length(); i++) {
      if (matches(sequence.charAt(i))) {
        count++;
      }
    }
    return count;
  }

  /**