pytorch: mixed models definition

mixed_models_definition.py

from torch import nn


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.feature_extractor = nn.Sequential(
            nn.Conv2d(3, 12, kernel_size=3, padding=1, stride=1),
            nn.Conv2d(12, 24, kernel_size=3, padding=1, stride=1),
        )
        self.second_extractor = nn.Conv2d(
            24, 36, kernel_size=3, padding=1, stride=1)

    def forward(self, x):
        x = self.feature_extractor(x)
        x = self.second_extractor(x)
        # note that we may call same layer twice or mode
        x = self.second_extractor(x)
        return x

if you call the second_extractor twice, then its shape may not match anymore, is it? Because for the second time, the input would be 24, but it expects 12.