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));
  }