LSTM ‘tuple’ object has no attribute ‘size’

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Continuing the discussion from LSTM 'tuple' object has no attribute 'size':

Hey thanks for posting this solution I also tried with the torchsummary from torchinfo. But still continued getting the error regarding the AttributeError: 'tuple' object has no attribute 'size'. Complete code of the model is here.
import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm1 = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=0.2)
        self.bn1 = nn.BatchNorm1d(hidden_size)
#         self.lstm2 = nn.LSTM(hidden_size, 32, batch_first=True, dropout=0.2)
        self.fc = nn.Linear(hidden_size, output_size)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        
        # Initialize hidden state with zeros
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device=x.device)
        
        # Initialize cell state with zeros
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device=x.device)
        
        #Xaviers initialization
        torch.nn.init.xavier_normal_(h0)
        torch.nn.init.xavier_normal_(c0)
        
        # Forward propagate LSTM1
        out, (h_final, c_final) = self.lstm1(x, (h0, c0))
#         out = self.bn1(out)
        
        # Forward propagate LSTM2
#         out, _ = self.lstm2(out)
        
        # Decode the hidden state of the last time step
        out = self.fc(out[:, -1, :])
        out = self.softmax(out)
        return out