<jupyter_start><jupyter_text>Augmentation from Tensorflow Addons<jupyter_code># image
Image.fromarray(image_ops.shear_x(np.asarray(image.convert("RGB")), 0.3, 0).numpy())
Image.fromarray(image_ops.(np.asarray(image.convert("RGB"))).numpy())
Image.fromarray(image_ops.equalize(np.asarray(image.convert("RGB"))).numpy())
Image.fromarray(image_ops.sharpness(np.asarray(image.convert("RGB")), 10).numpy())
Image.fromarray(image_ops.translate(np.asarray(image.convert("RGB")), [10, 10]).numpy())<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2, 'fraction_numerical': 0.01730769230769231, 'mean_word_length': 11.707317073170731, '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': '646544', 'license': 'MIT', 'n_tokens_mistral': 205, 'n_tokens_neox': 195, 'n_words': 15, 'path': 'source/pic2card/notebooks/Data_Augmentation.ipynb', 'repo_name': 'nikitawani07/AdaptiveCards', 'size': '520'}