from transformers import AdamW # Instantiate optimizer object optimizer = AdamW(model.parameters(), lr=2e-5) # Compute loss and gradients for the current batch loss.backward() # Set gradients to zero before backpropagation optimizer.zero_grad()This code initializes the `AdamW` optimizer with a learning rate of `2e-5`, and uses it to optimize the model's `parameters`. After computing the loss and gradients for the current batch, it sets the gradients to zero using `zero_grad()`, so that the optimizer does not accumulate previous gradients. In summary, the `zero_grad()` function is a commonly used tool when performing NLP tasks with the `transformers` library in Python. It is used to set the gradients to zero so that they do not accumulate between batches during training and optimization. The `AdamW` optimizer is an example of a package that can be used with this function to optimize the weights of the model.