'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,
    )

  // 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)))) {

            }
            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

    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;
      }
    }