The Transformer  is a type of attention-based model that uses self-attention mechanisms to process the input data. It consists of multiple encoder and decoder layers, each of which is made up of a multi-head self-attention mechanism and a fully-connected feedforward network. The Transformer layer takes in a sequence of input vectors and produces a sequence of output vectors. In the case of an image classification task, each input vector can represent a patch of the image, and the output vectors can be used to predict the class label for the image. How to build a Vision Transformer from Scratch Using Tensorflow   Building a Vision Transformer from scratch in TensorFlow can be a challenging task, but it is also a rewarding experience that can help you understand how this type of model works and how it can be used for image recognition and other computer vision tasks. Here is a step-by-step guide on how you can build a Vision Transformer in TensorFlow: Start by installing TensorFlow and