Error when applying 8-bits quantization on Object Detection model from Create ML using Transfer Learning

## 🐞Describing the bug
I've been trying to reduce the size of my Object Detection ML model generated by Xcode tool CreateML with Transfer Learning.
I found out that the transfer learning mlmodel output from Create ML is a **Mixed(Float32, Float16)** weights format.

I could successfully quantize this file to **Float16** format which saved 0.3MB already but I am not able to quantize the original model nor the **Float16** one down to 8-bits. I get a numpy error:
`ValueError: operands could not be broadcast together with shapes (0,1) (128,0)`

-> Is there a workaround to that error such as converting the **Mixed(Float32, Float16)** file up to full precision **Float32** prior to 8-bits quantization?

## Stack Trace
```
Quantizing using linear quantization
Optimizing Neural Network before Quantization:
Traceback (most recent call last):
File "/Users/username/Desktop/COMPRESSION/compression.py", line 5, in
quantized_model = quantization_utils.quantize_weights(mlmodel, 8)
File "/usr/local/lib/python3.9/site-packages/coremltools/models/neural_network/quantization_utils.py", line 1642, in quantize_weights
qspec = _quantize_spec_weights(spec, nbits, qmode, **kwargs)
File "/usr/local/lib/python3.9/site-packages/coremltools/models/neural_network/quantization_utils.py", line 1128, in _quantize_spec_weights
_quantize_spec_weights(model_spec, nbits, quantization_mode, **kwargs)
File "/usr/local/lib/python3.9/site-packages/coremltools/models/neural_network/quantization_utils.py", line 1113, in _quantize_spec_weights
_quantize_nn_spec(spec.neuralNetwork, nbits, quantization_mode, **kwargs)
File "/usr/local/lib/python3.9/site-packages/coremltools/models/neural_network/quantization_utils.py", line 723, in _quantize_nn_spec
_optimize_nn(layers)
File "/usr/local/lib/python3.9/site-packages/coremltools/models/neural_network/optimization_utils.py", line 213, in _optimize_nn
_conv_bn_fusion(int(conv_idx), int(output_idx), layers)
File "/usr/local/lib/python3.9/site-packages/coremltools/models/neural_network/optimization_utils.py", line 127, in _conv_bn_fusion
wp = (gamma / _np.sqrt(variance))[:, None] * w
ValueError: operands could not be broadcast together with shapes (0,1) (128,0)
```

## To Reproduce
Run this script on an object detection ML model generated with CreateML using transfer learning.
```
import coremltools as ct
from coremltools.models.neural_network import quantization_utils
mlmodel = ct.models.MLModel('objectDetection.mlmodel')
quantized_model = quantization_utils.quantize_weights(mlmodel, 8)
quantized_model.save("objectDetection-8bits.mlmodel")
```

## System environment:
- coremltools version: 6.1

Start with the provided quantization script and reproduce the failure using the Create ML object-detection model. Read coremltools/models/neural_network/quantization_utils.py and optimization_utils.py, especially _conv_bn_fusion, and trace the incompatible shapes; done means 8-bit quantization completes and saves the resulting model.

Written by the indexing model from the issue text.