SRGAN | Implementation of SRGAN SRResnet in PyTorch | Machine Learning library

While working with the code of SRGAN, I wanted to replace UpSampling2D by tf.image.resize_bicubic. I used keras lambda layer for this function, as below

I'm implementing SRGAN (and am not very experienced in this field), which uses a pre-trained VGG19 model to extract features. The following code was working fine on Keras 2.1.2 and tf 1.15.0 till yesterday. then it started throwing an "AttributeError: module 'keras.utils.generic_utils' has no attribute 'populate_dict_with_module_objects'" So i updated the keras version to 2.4.3 and tf to 2.5.0. but then its showing a "Input 0 of layer fc1 is incompatible with the layer: expected axis -1 of input shape to have value 25088 but received input with shape (None, 32768)" on the following line

I've been working on reimplementing Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN), now I'm stuck with the given information in section 3.2. Based on the paper, the target HR should be in the range [-1,1], the input LR should be in the range [0,1], and MSE loss was calculated on images in range [-1,1].

The last sentence implies that the output from the generator network should be in [-1,1]. So that the content_loss = MSELoss(generator(output), target). Am I understanding correctly? But when I print the output from my generator network, whose laster is just a conv2d, it gives me images in the rage [0,1].

I'm not getting a good result by running SRResNet-MSE part, and I think maybe because the MSE loss is calculating on different ranges instead of just one [-1,1]?

But how can the output from my generator be in range [-1,1] if I still want to follow paper's architecture, which has conv2d as the last layer?

I also include my code Class Generator here

How can I save a jupyter notebook with outputs? I'm editing my notebook with Google Collab and want to save it as a .ipynb file with outputs shown, like here: https://nbviewer.jupyter.org/github/krasserm/super-resolution/blob/master/example-srgan.ipynb.

But when I Download .ipynb on Google Collab, you can't see the outputs in the resulting file. How can I get the outputs to show?

Related question: how can I have the outputs save in the Google Collab doc? Right now, the outputs always disappear on reload, even though I disabled the hide outputs on save.

I have images that are having very low quality and these images I have to use for person identification but with this quality it's difficult to detect. I want to enhance the quality of the images using deep learning/machine learning techniques. I have studied about SRCNN, perceptual Loss, SRResNet, SRGAN but most of the super image resolution techniques require original images for improving the quality of the images. So my question is there any deep learning techniques that can be used for the improving the quality of the images without using the original images.

I want to use including and after tensorflow2.0 in Docker. I want to use (https://github.com/tensorlayer/srgan).

My Dockerfile is