x_shape = [label_to_size.get(x_label) for x_label in x_labels]
    y_shape = [label_to_size.get(y_label) for y_label in y_labels]
    bias_shape = None
    if use_bias:
      bias_shape = [label_to_size.get(out_label) for out_label in out_labels]
      bias_shape = bias_shape[-1:]
    contracting_dims = set()

    x_signature = list(x_shape)
    y_signature = list(y_shape)
    if generate_unknown_shape_signature:

  SmallVector<int64_t, 1> bias_shape{2};
  SmallVector<int64_t, 2> projection_shape{1, 2};
  SmallVector<int64_t, 1> layer_norm_scale{4};
  SmallVector<int64_t, 2> output_shape{1, 2};
  auto input_type = RankedTensorType::get(input_shape, builder->getF32Type());
  auto weight_type = RankedTensorType::get(weight_shape, builder->getF32Type());
  auto bias_type = RankedTensorType::get(bias_shape, builder->getF32Type());

    x_shape = [label_to_size.get(x_label) for x_label in x_labels]
    y_shape = [label_to_size.get(y_label) for y_label in y_labels]
    bias_shape = None
    if use_bias:
      bias_shape = [label_to_size.get(out_label) for out_label in out_labels]
      bias_shape = bias_shape[-1:]
    contracting_dims = set()

    x_signature = list(x_shape)
    y_signature = list(y_shape)

            num_bits=8,
            narrow_range=False,
        )
        return {'output': out}

    bias = None
    if has_bias:
      bias_shape = shapes[1][-1]
      if bias_shape is not None:
        bias = array_ops.constant(
            np.random.uniform(size=[shapes[1][-1]]), dtype=dtypes.float32
        )
    model = MatmulModel(bias)
    x = array_ops.constant(

        filter_quantized_element_type.getZeroPoint());
  }

  SmallVector<int64_t, 1> bias_shape = {filter_shape[0]};
  auto bias_type =
      RankedTensorType::getChecked(loc, bias_shape, bias_quantized_type);

  auto bias_value_type = RankedTensorType::getChecked(
      loc, std::move(bias_shape), rewriter.getI32Type());
  auto bias_value = DenseIntElementsAttr::get(

      self,
      equation: str,
  ):
    _, y_shape, bias_shape, x_signature, y_signature = (
        self._prepare_sample_einsum_datashapes(equation, use_bias=True)
    )

    model = self._create_einsum_model(
        self._input_saved_model_path,
        equation,
        y_shape,
        x_signature,
        y_signature,
        bias_shape,
    )

    # Generate model input data.

    Operation* op, const int bias_index,
    const ArrayRef<int> non_bias_operand_indices,
    const AccumulatorScaleFunc func) {
  QuantState& bias_state = GetOperandQuantState(op, bias_index);
  if (!bias_state.IsEmpty()) {
    return bias_state.params;
  }
  std::vector<QuantizedType> op_types{};
  op_types.reserve(non_bias_operand_indices.size());

  int adjusted_quant_dim = -1;

    auto bcast_op_result_type =
        mlir::cast<RankedTensorType>(bcast_op_result.getType());
    const ArrayRef<int64_t> bcast_shape = bcast_op_result_type.getShape();
    const TensorType new_bcast_op_result_type = bcast_op_result_type.cloneWith(
        bcast_shape, accumulation_quantized_element_type);
    bcast_op_result.setType(new_bcast_op_result_type);
  }

  const auto add_op_result_type =

}

std::optional<RankedTensorType> InferWindowOutputShape(
    const ShapedType& base_shape, const xla::Window& window,
    Type element_type) {
  if (window.dimensions_size() != base_shape.getRank()) {
    llvm::errs() << "Window has dimension " << window.dimensions_size()
                 << " but base shape has dimension " << base_shape.getRank()
                 << "\n";
    return std::nullopt;
  }

  for (size_t i = 0; i < out_channel_size; i++) {
    const float bias_scale = disable_per_channel_quantization_for_dense_
                                 ? bias_scales[0]
                                 : bias_scales[i];
    auto dequantized_value = bias_values[i] * bias_scale;
    EXPECT_THAT(dequantized_value,
                FloatNear(bias_float_buffer[i], bias_scale / 2));
  }