My model is like this:
class hybrid_cnn(nn.Module):
    def __init__(self,**kwargs):
        super(hybrid_cnn,self).__init__()
        resnet = torchvision.models.resnet50(pretrained=True)
        self.base = nn.Sequential(*list(resnet.children())[:-2]

        return clf_outputs

I have two pathways named as augmented1 and augmented2 which are a set of convolutional layers. Now i want to feed two datasets (Dataset A and Dataset B) to my base model which would then be passed on two these two (augmented1 and augmented2 ) pathways. So this could only be done with batches. So i want to have batch size of 64, 32 samples from DatasetA need to go to pathway1 (base model->pathway1) and another 32 samples from other DatasetB need to go through pathway2 (base model->pathway2). Base Model is common to both pathways. Also i want to pick up batches in the same order when i am setting shuffle=True since I will be extracting feature maps from both pathways