Mat1 and mat2 shapes cannot be multiplied (8x10 and 8x8)

[I know this question comes a lot, but I could not find any answer matching my use case]

I feed N = 10 inputs made out of 8 floats to a 8 input layer

but I get that error

Mat1 and mat2 shapes cannot be multiplied (8x10 and 8x8) 

What am I missing ?

I generate random data in my dataset

class MyDataset(Dataset):    def __init__(self):        self.data = []        self.input_size = 0        for i in range(0,10):            label = random.randint(0, 1)            data = [random.uniform(0.0, 1.0) for _ in range(8)]            self.data.append((data , label))            self.input_size = len(data ) if self.input_size < len(encoded_text) else self.input_size    def __len__(self):        return len(self.data)    def __getitem__(self, idx):        text, label = self.data[idx]        return text, label

This is my model

class MyModel(nn.Module):    def __init__(self, input_size, hidden_size, output_size):        super(MyModel, self).__init__()        self.input = nn.Linear(input_size,hidden_size)        self.hidden = nn.Linear(hidden_size, output_size)        self.sigmoid = nn.Sigmoid()    def forward(self, x):        # padding is there as original dataset does not have full 8 floats inputs        x_padded = pad_sequence(x, batch_first=True, padding_value=0).float()          output = self.input(x_padded) >>>>>>>> ERROR        return torch.sigmoid(output)def train_model(model, train_loader, criterion, optimizer, num_epochs):    for epoch in range(num_epochs):        for inputs, labels in train_loader:            outputs = model(inputs)

The initialisation part

if __name__ == "__main__":    hidden_size = 8  # hidden size     output_size = 1  # binary classification     learning_rate = 0.001    num_epochs = 10    dataset = MyDataset()    train_loader = DataLoader(dataset, batch_size=64, shuffle=True)    input_size = dataset.input_size    model = MyModel(input_size, hidden_size, output_size)    criterion = nn.BCEWithLogitsLoss()    optimizer = optim.Adam(model.parameters(), lr=learning_rate)    train_model(model, train_loader, criterion, optimizer, num_epochs)`

This is a really simple beginner use case, nothing fancy

thanks for your help