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Solve the following python programming problem given the following code:
# For data loading.
from torchtext import data, datasets

!pip install torchtext spacy
!python -m spacy download en
!python -m spacy download de

# Load words from IWSLT

#!pip install torchtext spacy
#!python -m spacy download en
#!python -m spacy download de

import spacy
spacy_de = spacy.load('de')
spacy_en = spacy.load('en')

def tokenize_de(text):
    return [tok.text for tok in spacy_de.tokenizer(text)]

def tokenize_en(text):
    return [tok.text for tok in spacy_en.tokenizer(text)]

BOS_WORD = '<s>'
EOS_WORD = '</s>'
BLANK_WORD = "<blank>"
SRC = data.Field(tokenize=tokenize_de, pad_token=BLANK_WORD)
TGT = data.Field(tokenize=tokenize_en, init_token = BOS_WORD, 
                 eos_token = EOS_WORD, pad_token=BLANK_WORD)

MAX_LEN = 100
train, val, test = datasets.IWSLT.splits(exts=('.de', '.en'), fields=(SRC, TGT), 
                                         filter_pred=lambda x: len(vars(x)['src']) <= MAX_LEN and 
                                         len(vars(x)['trg']) <= MAX_LEN)
MIN_FREQ = 1
SRC.build_vocab(train.src, min_freq=MIN_FREQ)
TGT.build_vocab(train.trg, min_freq=MIN_FREQ)

# Detail. Batching seems to matter quite a bit. 
# This is temporary code for dynamic batching based on number of tokens.
# This code should all go away once things get merged in this library.

BATCH_SIZE = 4096
global max_src_in_batch, max_tgt_in_batch
def batch_size_fn(new, count, sofar):
    "Keep augmenting batch and calculate total number of tokens + padding."
    global max_src_in_batch, max_tgt_in_batch
    if count == 1:
        max_src_in_batch = 0
        max_tgt_in_batch = 0
    max_src_in_batch = max(max_src_in_batch,  len(new.src))
    max_tgt_in_batch = max(max_tgt_in_batch,  len(new.trg) + 2)
    src_elements = count * max_src_in_batch
    tgt_elements = count * max_tgt_in_batch
    return max(src_elements, tgt_elements)

class MyIterator(data.Iterator):
    def create_batches(self):
        if self.train:
            def pool(d, random_shuffler):
                for p in data.batch(d, self.batch_size...
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python program to calculate total number of tokens + padding.

A Real World Example