Thanks for the code.
The error is raised, since you are assigning a tuple to self.unpool1 (and the following layers) by adding a comma after nn.MaxUnpool2d(2),.
If you remove it, you would get a shape mismatch, since INCIDES_2 are expected in self.unpool1.
Also, self.ReLU is undefined, so I assume you would like to use self.relu3 instead. You are currently creating self.relu3 two times, so I guess the last one should be self.relu4.
Here is the model with the mentioned fixes:

class AutoEncoder(nn.Module):
    def __init__(self):
        super(AutoEncoder, self).__init__()
    
        #encoder
        self.conv1 = nn.Conv2d(1, 16, 5, 1, 2)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(2, return_indices=True)
        self.conv2 = nn.Conv2d(16, 32, 5, 1, 2)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(2, return_indices=True)
        #decoder
        self.unpool1 = nn.MaxUnpool2d(2)
        self.convT1 = nn.ConvTranspose2d(32, 16, 5, 1, 2)
        self.relu3 = nn.ReLU()
        self.unpool2 = nn.MaxUnpool2d(2)
        self.convT2 = nn.ConvTranspose2d(16, 1, 5, 1, 2)
        self.relu4 = nn.ReLU()
    
    def forward(self, x):
        CONV_1 = self.conv1(x)
        RELU_1 = self.relu1(CONV_1)
        POOL_1, INDICES_1 = self.pool1(RELU_1)
        CONV_2 = self.conv2(POOL_1)
        RELU_2 = self.relu2(CONV_2)
        POOL_2, INDICES_2 = self.pool2(RELU_2)
        encoded = POOL_2
        UNPOOL_1 = self.unpool1(POOL_2 , INDICES_2)
        CONVT_1 = self.convT1(UNPOOL_1)
        RELU_3 = self.relu3(CONVT_1)
        UNPOOL_2 = self.unpool2(RELU_3, INDICES_1)
        CONVT_2 = self.convT2(UNPOOL_2)
        RELU_4 = self.relu4(CONVT_2)
        decoded = RELU_4
        return encoded, decoded

model = AutoEncoder()
x = torch.randn(1, 1, 224, 224)
out = model(x)