'has_bias': True,
      },
  )
  def test_conv2d_ptq_model_whole_model_verify(self, activation_fn, has_bias):
    input_shape = [None, None, None, 3]
    filter_shape = [2, 3, 3, 2]

    model = self._create_conv2d_model(
        input_shape,
        filter_shape,
        activation_fn=activation_fn,
        has_bias=has_bias,
    )

            [3, None],
        ],
        'has_bias': [True, False],
    }]
    combinations = testing.parameter_combinations(test_parameters)

    self.assertLen(combinations, 4)
    self.assertIn({'shapes': [3, 3], 'has_bias': True}, combinations)
    self.assertIn({'shapes': [3, 3], 'has_bias': False}, combinations)
    self.assertIn({'shapes': [3, None], 'has_bias': True}, combinations)

    # If bias_size is not explictly given, it should default to width of weight.
    if bias_size is None and has_bias:
      bias_size = weight_shape[-1]

    # Verify that when bias_size is not None, has_bias should be True.
    # And if bias_size is None, has_bias should be False using XNOR
    assert not ((bias_size is not None) ^ has_bias)

    # Verify that bias size is correct
    if bias_size:

  // Bias tensor is optional.
  const bool has_bias = !(!getBias() || getBias().getType().isa<NoneType>());

  // Get the tensors.
  DenseElementsAttr input_tensor, weights_tensor, bias_tensor;
  if (!matchPattern(getInput(), m_Constant(&input_tensor)) ||
      !matchPattern(getFilter(), m_Constant(&weights_tensor)) ||
      (has_bias && !matchPattern(getBias(), m_Constant(&bias_tensor)))) {

   */
  static void checkAvalanche(HashFunction function, int trials, double epsilon) {
    Random rand = new Random(0);
    int keyBits = 32;
    int hashBits = function.bits();
    for (int i = 0; i < keyBits; i++) {
      int[] same = new int[hashBits];
      int[] diff = new int[hashBits];
      // go through trials to compute probability
      for (int j = 0; j < trials; j++) {
        int key1 = rand.nextInt();

            }
            int hashBits = prototype.getDigestLength() * 8;
            try {
                Object ignored = prototype.clone();
                return new CloningMessageDigestHashFunction(prototype, hashBits);
            } catch (CloneNotSupportedException e) {
                return new RegularMessageDigestHashFunction(algorithm, hashBits);
            }
        }

        @Override

                    f, cloned_types=cloned_types
                )
    new_field = create_response_field(name=field.name, type_=use_type)
    new_field.has_alias = field.has_alias  # type: ignore[attr-defined]
    new_field.alias = field.alias  # type: ignore[misc]
    new_field.class_validators = field.class_validators  # type: ignore[attr-defined]

	// stop things from getting too large.
	maxFlateBlockTokens = 1 << 14
	maxStoreBlockSize   = 65535
	hashBits            = 17 // After 17 performance degrades
	hashSize            = 1 << hashBits
	hashMask            = (1 << hashBits) - 1
	maxHashOffset       = 1 << 24

	skipNever = math.MaxInt32
)

type compressionLevel struct {
	level, good, lazy, nice, chain, fastSkipHashing int

ha
hemos
habéis
han
haya
hayas
hayamos
hayáis
hayan
habré
habrás
habrá
habremos
habréis
habrán
habría
habrías
habríamos
habríais
habrían
había
habías
habíamos
habíais
habían
hube
hubiste
hubo
hubimos
hubisteis
hubieron
hubiera
hubieras
hubiéramos
hubierais
hubieran
hubiese
hubieses
hubiésemos
hubieseis

    bool needs_adjustment = false;
    for (int i = 0; i < bias_quantized_type.getScales().size(); ++i) {
      const float abs_bias = std::abs(bias_values.getValues<float>()[i]);
      if (abs_bias / new_bias_scales[i] > kBiasMax) {
        new_bias_scales[i] = static_cast<double>(abs_bias) / kBiasMax;
        new_filter_scales[i] = new_bias_scales[i] / input_scale;
        needs_adjustment = true;
      }
    }