<jupyter_start><jupyter_text>Random inducing Adversarial inducing Test input as inducing<jupyter_code>X_train.shape
Y_train.shape
class ffn(nn.Module):
    def __init__(self):
        super(ffn, self).__init__()
        self.l1 = nn.Linear(13, 50, bias = True)
        self.l2 = nn.Linear(50, 1)
        
    def forward(self, x):
        x = F.relu(self.l1(x))
        x = self.l2(x)
        return x

#net_fn = timm.create_model('mixer_b16_224', pretrained=True)
net_fn = ffn()
for name, module in net_fn.named_modules():
    if isinstance(module, nn.Linear):
        print(name, module)<jupyter_output>l1 Linear(in_features=13, out_features=50, bias=True)
l2 Linear(in_features=50, out_features=1, bias=True)
{'fraction_non_alphanumeric': 0.1279887482419128, 'fraction_numerical': 0.035161744022503515, 'mean_word_length': 3.65359477124183, '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': '36417', 'license': 'Apache-2.0', 'n_tokens_mistral': 284, 'n_tokens_neox': 266, 'n_words': 54, 'path': 'example/regression_boston.ipynb', 'repo_name': 'nonconvexopt/pytorch-FBNN', 'size': '711'}