<jupyter_start><jupyter_text><jupyter_code>import torch
import torch.nn as nn
pad = nn.ReflectionPad2d(1)
a = torch.arange(16, dtype=torch.float32).reshape((1,1,4,4))
print(a)
print(pad(a))
a = torch.ones(1,1,3,3, requires_grad=True)
def printGrad(grad):
  print(grad)
b = pad(a)
b.register_hook(printGrad)
c = b.sum()
c.backward()
a.grad<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17391304347826086, 'fraction_numerical': 0.03804347826086957, 'mean_word_length': 7.5813953488372094, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '324349', 'license': 'MIT', 'n_tokens_mistral': 166, 'n_tokens_neox': 164, 'n_words': 26, 'path': 'ReflectionPad2d_CaseStudy.ipynb', 'repo_name': 'EighteenSprings/BaiduPaperReproduction2end', 'size': '368'}