quantize_graph function in slim does not add FakeQuantWithMinMaxVars around conv2d_transpose.

1. The entire URL of the file you are using

https://github.com/tensorflow/models/blob/master/research/slim/train_image_classifier.py

2. Describe the bug

The contrib_quantize.create_training_graph(quant_delay=FLAGS.quantize_delay) in the train_image_classifier.py does not add FakeQuantWithMinMaxVars around tf.layers.conv2d_tranpose, slim.layers.conv2d_tranpose or even tf.nn.conv2d_tranpose. Because of this, the model can not be quantized using TOCO

3. Steps to reproduce

Train any model with conv2d_transpose layer in it using the train_image_classifier.py. Then switch on the quantization and try to convert it into a TFLITE model using TOCO. For example, in the pix2pix net, there is conv2d_tranpose and it can not be quantized.

4. Expected behavior

It should add the FakeQuantWithMinMaxVars since it does not state anywhere that it only works on some operations.

5. Additional context

The error from TOCO:
...conv2d_transpose, which is an input to the Relu operator producing the output array MobilenetV1/MobilenetV1/first_transpose_conv/Relu, is lacking min/max data, which is necessary for quantization. If accuracy matters, either target a non-quantized output format, or run quantized training with your model from a floating point checkpoint to change the input graph to contain min/max information. If you don't care about accuracy, you can pass --default_ranges_min= and --default_ranges_max= for easy experimentation.
Fatal Python error: Aborted

6. System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): 18.04
• Mobile device name if the issue happens on a mobile device:
• TensorFlow installed from (source or binary): binary
• TensorFlow version (use command below): 1.15.0, 1.15.2, 1.15.3, 1.15.4
• Python version: 3.6.8
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version: 10.0 and 7.6.5
• GPU model and memory: NVIDIA GTX1080 Ti 11Gb and NVIDIA RTX2080 Ti 11Gb