mixed-precision-calibration (int8 and fp16-precision-Layers) with TensorRT

I am currently working on a Variational Autoencoder. It is fully trained in fp32-precision.
Calibrating the entire network on int8 with the „IInt8EntropyCalibrator2“works great.
However, there are some small problems with the output-precision. The problem can be solved by a higher precision in the sampling layer (I checked that by using the int8 calibration and set the dynamic-range manually). Can do this in one step with a Calibrator?
My question or problem relates to the calibrator, whether it is capable of mixed-precision.
The Builder cannot build the engine with the calibrator and mixed-precision. The mixed-precision is done with the following Code:

Layer_with_high_precision=list(range(70,100))#Layer 70 up to 100 as float32-Precision

for i in range(network.num_layers): #go through all the layers
    layer = network[i]
    print('Layer:'+str(i)+'\t Name:'+str(layer.name)+'\t\t Precision:'+str(layer.precision)+'\t Typ:'+str(layer.type))
    if i in Layer_with_high_precision:
        '''specify the layer precision (want to run certain layers a specific precision)
        This gives the layer’s inputs and outputs a preferred type'''
        layer.precision = trt.float32
        '''You can choose a differentpreferred type for an output of a layer using
        set the output tensor data type to conform with the layer implementation'''
        if  i+1 not in Layer_with_high_precision:# if the Next-Layer is int8
            for j in range(layer.num_outputs):
                layer.set_output_type(j, trt.int8)
        if i+1 in Layer_with_high_precision: # if the Next-Layer is float32
            for j in range(layer.num_outputs):
                layer.set_output_type(j, trt.float32)

builder.strict_type_constraints = True

Do I have to do the calibration myself by hand?
Can the calibrator also calibrate mixed precision or is this not possible?
The documentation is not clear about that.