```py
if args.modifier_token is not None:
if accelerator.num_processes > 1:
grads_text_encoder = text_encoder.module.get_input_embeddings().weight.grad
else:
grads_text_encoder = text_encoder.get_input_embeddings().weight.grad
index_grads_to_zero = torch.arange(len(tokenizer)) != modifier_token_id[0]
for i in range(len(modifier_token_id[1:])):
index_grads_to_zero = index_grads_to_zero & (
torch.arange(len(tokenizer)) != modifier_token_id[i]
)