model.eval() sets the Dropout (and Normalization layers, if any) in evaluation mode. The default mode is for training. In the trining mode, the elements of input tensors are zeroed with probability p, but in the evaluation mode all elements are used as is. So, when you put the model in the eval() model, then the Dropout layer becomes ineffective in training, so it’s just like removing it entirely or setting p=0.
Removing Dropout layer is expected to lower the training loss, since the elements of input tensors are not randomly zeroed. However, removing Dropout may result in poor performance on the unseen test set, since now the model is more prone to overfitting.
Therefore, you should consider treating the Dropout parameter as a hyperparameter, and you can tune it by studying the performance of a validation set.