from torchtext.datasets import Multi30k from torchtext.data import Field, BucketIterator SRC = Field(tokenize='spacy', tokenizer_language='de', init_token='In this code snippet, we imported the Multi30k dataset from torchtext.datasets and created two Field objects for the source and target languages. We then defined our BucketIterator by specifying the batch size and the device we will use for computation. Another example where BucketIterator can come in handy is when we want to perform sentiment analysis on textual reviews, we can use the BucketIterator to sort them into buckets according to the length of the review, making it a faster and more efficient way to iterate through our dataset. In conclusion, the BucketIterator is a powerful tool in the torchtext.data package in Python for efficient and effective processing of text-based datasets.', eos_token=' ', lower=True) TRG = Field(tokenize='spacy', tokenizer_language='en', init_token=' ', eos_token=' ', lower=True) train_data, valid_data, test_data = Multi30k.splits(exts=('.de', '.en'), fields=(SRC, TRG)) BATCH_SIZE = 512 train_iterator, valid_iterator, test_iterator =BucketIterator.splits((train_data, valid_data, test_data), batch_size=BATCH_SIZE, device='cuda')