seg_results.append(predicted.cpu().numpy().reshape(-1, 400))

This line of code appends the predicted results of a machine learning model to a list called "seg_results". The predicted values are in the form of a numpy array, which is first converted to a CPU tensor using the "cpu()" method. The tensor is then reshaped to have a single dimension of size 400, and appended to the list.

This code is likely used in a segmentation task, where the goal is to classify every pixel in an image as belonging to a certain class (e.g. object vs. background). The model would produce a predicted output for each pixel in the image, which would be flattened into a 1D array of length 400 (assuming the image is 20x20 pixels). These arrays would be collected in "seg_results", which could then be used for evaluation or further processing.