x_valid,x_train=x_train[:3000],x_train[3000:] y_valid,y_train=y_train[:3000],y_train[3000:]

These lines of code are splitting the `x_train` and `y_train` datasets into two parts: `x_valid` and `y_valid` (with 3000 samples) and `x_train` and `y_train` (with the remaining samples).

This is a common practice in machine learning to have a validation set that is used to tune the model hyperparameters and ensure that the model is not overfitting to the training data. The validation set is used to evaluate the model performance during the training process.

By splitting the dataset in this way, the model can be trained on `x_train` and `y_train` and the performance can be evaluated on `x_valid` and `y_valid`. Once the model is trained and validated, it can be tested on a completely new set of data.