, there is a file named 2nd_degree_regression_MISO_TO_DO. In this
file, you are tasked with implementing a 2nd-degree linear regression model for a Multiple-Input-
Single-Output (MISO) problem. The objective is to model the relationship between two input
variables and one output variable using polynomial regression.
Task Breakdown:

How can I generate overfitting on noisy regression data using PyTorch? Despite various attempts, PyTorch tends to generalize the data, making it challenging to memorize (overfit) the data. I modified weight initialization, used batch_size=1, made the learning rate flexible, and increased the number of layers and parameters, but it still generalizes.
Are there any parameters to be changed?