I want to convert float32 model to 8-bit integer by using post training quantization. And follow the webs page and use tflite convertor to do that.  But some operator may cause error,  because it does not comply with the restriction of int8 spec
In case of dense layer:
if dense layer with no bias or zero bias, it will cause error. The message shows below
tensorflow/lite/kernels/kernel_util.cc:106 std::abs(input_product_scale - bias_scale) <= 1e-6 * std::min(input_product_scale, bias_scale)

I found the bias scale is 0, so, that error occur. I need to specified the bias scale to avoid that problem.
In case of slice or stride_slice:
If I concatenate multiple stride_slice tensor.  The scale of all concatenated elements has different scale.
Thus,  i want to specified the scale and zero point to make sure the model is comply with int8 spec.
Is there any API supported to do that?  thanks!