Hello,
I was experimenting with load_state_dict with strict = False. Before my asking my query, I will post my model code here
class AutoEncoder(nn.Module):
    def  __init__(self, input_dim):
        super(AutoEncoder, self).__init__()

        self.input_dim = input_dim
        # encoder
        self.batchnorm1 = nn.BatchNorm1d(self.input_dim)
        self.linear1 = nn.Linear(self.input_dim, 64)
        self.batchnorm2 = nn.BatchNorm1d(64)
        self.linear2 = nn.Linear(64, 32)
        # decoder
        self.batchnorm3 = nn.BatchNorm1d(32)
        self.linear3 = nn.Linear(32, 64)
        self.batchnorm4 = nn.BatchNorm1d(64)
        self.linear4 = nn.Linear(64, self.input_dim)
        
        # relu
        self.relu = nn.ReLU()
    
    def forward(self, h0):
        # encoder forward pass
        h1 = self.relu(self.linear1(self.batchnorm1(h0)))
        h2 = self.linear2(self.batchnorm2(h1))
        # decoder forward pass
        h3 = self.relu(self.linear3(self.batchnorm3(h2)))
        h4 = self.linear4(self.batchnorm4(h4))
        
        return h4
    
class Classifier(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(Classifier, self).__init__()

        self.input_dim = input_dim
        self.output_dim = output_dim
        # encoder
        self.batchnorm1 = nn.BatchNorm1d(self.input_dim)
        self.linear1 = nn.Linear(self.input_dim, 64)
        self.batchnorm2 = nn.BatchNorm1d(64)
        self.linear2 = nn.Linear(64, 32)
        
        self.batchnorm = nn.BatchNorm1d(32)
        self.classifer = nn.Linear(32, self.output_dim)
    
    def forward(self, h0):
        h1 = self.relu(self.linear1(self.batchnorm1(h0)))
        h2 = self.linear2(self.batchnorm2(h1))
        
        out = self.classifier(self.batchnorm(h2))
        return out

After this I intialized both the models as shown below
autoencoder_model = AutoEncoder(130)
classifier_model = Classifier(130, 1)

After this I tried this classifier_model.load_state_dict(autoencoder_model.state_dict(), strict=False). That is it will load the encoder part of autoencoder weights to the classifier. This is the output I got (which shows the missing keys and unexpected keys)
_IncompatibleKeys(missing_keys=['batchnorm.weight', 'batchnorm.bias', 
'batchnorm.running_mean', 'batchnorm.running_var', 'classifer.weight', 'classifer.bias'],
 unexpected_keys=['batchnorm3.weight', 'batchnorm3.bias', 'batchnorm3.running_mean', 
'batchnorm3.running_var', 'batchnorm3.num_batches_tracked', 'linear3.weight', 
'linear3.bias', 'batchnorm4.weight', 'batchnorm4.bias', 'batchnorm4.running_mean',
 'batchnorm4.running_var', 'batchnorm4.num_batches_tracked', 'linear4.weight', 
'linear4.bias'])

The output also looks correct, but my query is why does “batchnorm.num_batches_tracked” didn’t come in missing keys ?