@ptrblck Thank you for clarifying but I still can not train the model.  I am trying to access each sample with loop but it needed the batch dimension:
for epoch in range(epochs):
    for i, batch in enumerate(loader):
        for item in range(batch_size):
            original_list, target_list = batch
            original = original_list[item][0]
            target = target_list[item][0]
            
            out = model(original)

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-95-2249137957c2> in <module>
     19             target = target_list[item][0]
     20 
---> 21             out = model(original)

C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

<ipython-input-7-5f743c3455c4> in forward(self, x)
     89         # encoder pathway, save outputs for merging
     90         for i, module in enumerate(self.down_convs):
---> 91             x, before_pool = module(x)
     92             encoder_outs.append(before_pool)
     93 

C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

<ipython-input-5-26a0f7e21ea6> in forward(self, x)
     14 
     15     def forward(self, x):
---> 16         x = F.relu(self.conv1(x))
     17         x = F.relu(self.conv2(x))
     18         before_pool = x

C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
    336                             _pair(0), self.dilation, self.groups)
    337         return F.conv2d(input, self.weight, self.bias, self.stride,
--> 338                         self.padding, self.dilation, self.groups)
    339 
    340 

RuntimeError: Expected 4-dimensional input for 4-dimensional weight 64 3 3, but got 3-dimensional input of size [3, 321, 481] instead


And if I access first dimension, an error occurs saying that the input should tensor not list:
for epoch in range(epochs):
    for i, batch in enumerate(loader):
        for item in range(batch_size):
            original_list, target_list = batch
            original = original_list[item]
            out = model(original)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-103-1b36111ac68f> in <module>
     19             target = target_list[item][0]
     20 
---> 21             out = model(original)

C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

<ipython-input-7-5f743c3455c4> in forward(self, x)
     89         # encoder pathway, save outputs for merging
     90         for i, module in enumerate(self.down_convs):
---> 91             x, before_pool = module(x)
     92             encoder_outs.append(before_pool)
     93 

C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

<ipython-input-5-26a0f7e21ea6> in forward(self, x)
     14 
     15     def forward(self, x):
---> 16         x = F.relu(self.conv1(x))
     17         x = F.relu(self.conv2(x))
     18         before_pool = x

C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
    336                             _pair(0), self.dilation, self.groups)
    337         return F.conv2d(input, self.weight, self.bias, self.stride,
--> 338                         self.padding, self.dilation, self.groups)
    339 
    340 

TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not tuple

How can I pass the batch dimension? Can you please explain?
Thanks a lot