Adding new feature to perform reverse operation of tf.image.extract_patches
@ash-S26 
Yes, TensorFlow doesn't offer a direct inverse function for tf.image.extract_patches, here are several approaches to reconstruct an image or approximate the reverse operation as Gradient-Based Reconstruction:

1. Use Gradient Tape: Employ tf.GradientTape to compute gradients of a loss function with respect to the extracted patches.
2. Update Patches: Iteratively update the patches to minimize the loss, aiming to reconstruct the original image.

Please let us know more on the feature request you are proposing here with its use-case.  
Thank you! I conceived the idea when combining the patch merging concept from the Swin Transformer paper (https://arxiv.org/pdf/2103.14030.pdf) with the SwinIR Transformer (https://arxiv.org/pdf/2108.10257.pdf).


**Background Explanation: -**
The Swin Transformer is an architecture designed for image classification. It starts with an image, for example, of size (224,224,3), divides it into patches of size (4,4), creating a new dimensional vector of shape (56,56,344). Attention is then calculated within small windows of size 7. In the next stage, patch merging combines adjacent patches (e.g., at indices [0,0], [0,1], [1,0], [1,1]), resulting in an output shape of (56/2,56/2,344*4). The process is repeated, and the final stage includes flattening the layers.

Currently, the SwinIR Transformer, designed for image super-resolution, does not utilize the patch merging concept. It maintains the same image size while calculating attention within a single window of size 7 and a small shifted window.


**Use-Case (Why this feature came to my mind): -**
The goal is to integrate the benefits of patch merging into SwinIR, enabling the capture of global-level attention with reduced computation cost, thus enhancing the model. During the implementation, I observed an easy way to reach the last merging step but lacked a tool in TensorFlow to reverse the process (i.e., un-patch the image), placing pixels back in their original positions. This approach results in the next block containing pixels in their original positions but with more context/attention captured over the image. I raised this issue to propose the addition of this feature.

Also I am enthusiastic about contributing to the implementation of Swin Transformer and SwinIR Transformer for the official TensorFlow repository. I would appreciate the opportunity to create and contribute this feature. 

Please let me know if you need additional information on this topic.

Thank you!
Need of feature :- 
Will increase use of transformers in Computer Vision task, we need a way to efficient apply attention mechanism for images (unlike vision transformer which calculate attention for entire image). Here we can calculate attention over extracted patches for **purpose of Image Super-resolution, Image Restoration**. Currently there is no feature in Tensorflow to do reverse operation of tf.image.extract_patches, hence I would like to contribute to this feature.

Inspiration for feature :- 
Swin Transformer

I would like to contribute for this feature. Could you please assign this task to me.

Thank you