<jupyter_start><jupyter_text><jupyter_code>import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread('blm1619.jpg')
print(img)<jupyter_output>[[[ 21  20  25]
  [ 25  24  29]
  [ 26  25  30]
  ...
  [ 10  18  20]
  [ 12  20  22]
  [ 16  24  26]]

 [[ 23  23  25]
  [ 32  32  34]
  [ 37  37  39]
  ...
  [ 10  18  20]
  [  2  10  12]
  [ 20  28  30]]

 [[ 32  30  33]
  [ 38  36  39]
  [ 43  41  44]
  ...
  [ 35  43  45]
  [ 25  33  35]
  [ 27  35  37]]

 ...

 [[180 164 138]
  [184 168 142]
  [184 169 140]
  ...
  [244 201  63]
  [248 205  67]
  [251 208  70]]

 [[181 165 139]
  [181 166 137]
  [182 167 138]
  ...
  [242 199  61]
  [249 206  68]
  [253 210  72]]

 [[184 169 140]
  [180 165 136]
  [180 165 136]
  ...
  [240 197  59]
  [249 206  68]
  [254 211  73]]]
{'fraction_non_alphanumeric': 0.15809284818067754, 'fraction_numerical': 0.3312421580928482, 'mean_word_length': 1.6688963210702341, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '324530', 'license': 'MIT', 'n_tokens_mistral': 588, 'n_tokens_neox': 383, 'n_words': 119, 'path': 'matplotimageload.ipynb', 'repo_name': 'devcmgm/pytorch101', 'size': '797'}