print(self.inputs) has a shape of (665, 5) and the following structure:
[[tensor([292.934])  tensor([134], dtype=torch.int32)
  tensor([1.1, 1.2, 1.3, 1.4, ..., 1.153]) # DU_DY, size of 153
  tensor([2.1, 2.2, 2.3, 2.4, ..., 2.153]) # Y, size of 153
  tensor([550.123])] # this Re_tau input determines size for DU_DY & Y
 [tensor([50.433])  tensor([97], dtype=torch.int32)
  tensor([1.1, 1.2, 1.3, 1.4, ..., 1.256]) # DU_DY, size of 256
  tensor([2.1, 2.2, 2.3, 2.4, ..., 2.256]) # Y, size of 256
  tensor([1000.23])] # Re_tau input determines size for DU_DY & Y
 [another training data with different DU_DY & Y size]
 ...
 [another training data with different DU_DY & Y size]]


print(self.target) has a shape of (665, 1) this structure:
[[tensor([-1.3254])]
 [tensor([-1.9446])]
 ...
 [tensor( [-1.4099])]
 [tensor( [-1.4342])]
 [tensor( [0.])]]


Before getting rid of the list() in class ChannelDataset: element in dataset ds_train has the same list format as ds_train[0]:
([tensor([292.934]), tensor([134], dtype=torch.int32), tensor([1.1, 1.2, 1.3, 1.4, ..., 1.153]), tensor([2.1, 2.2, 2.3, 2.4, ..., 2.153]), tensor([550.123])], tensor([-1.3254])) 


If I get rid of the list() in ChannelDataset(), the error message upon execution is
TypeError                                 Traceback (most recent call last)
<ipython-input-4-4e3f094c5224> in <module>
     35 
     36 '''Fit model'''
---> 37 trainer.fit(df_train, df_val, df_test, batch_size=3, print_freq=1, max_epochs=1, earlystopping=False, patience=30)
     38 
     39 scores = {'Train': evaluate(trainer, df_train),

~\Desktop\IACS\Code\Demo\utils_train.py in fit(self, df_train, df_val, df_test, batch_size, print_freq, max_epochs, min_epochs, earlystopping, patience)
    246 
    247             # Loop over trainloader
--> 248             losses, nums = zip(*[self._step(data_batch, self._optimizer) for data_batch in trainloader])
    249             train_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)
    250 

~\Desktop\IACS\Code\Demo\utils_train.py in <listcomp>(.0)
    246 
    247             # Loop over trainloader
--> 248             losses, nums = zip(*[self._step(data_batch, self._optimizer) for data_batch in trainloader])
    249             train_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)
    250 

~\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py in __next__(self)
    343 
    344     def __next__(self):
--> 345         data = self._next_data()
    346         self._num_yielded += 1
    347         if self._dataset_kind == _DatasetKind.Iterable and \

~\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py in _next_data(self)
    383     def _next_data(self):
    384         index = self._next_index()  # may raise StopIteration
--> 385         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    386         if self._pin_memory:
    387             data = _utils.pin_memory.pin_memory(data)

~\Anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py in fetch(self, possibly_batched_index)
     45         else:
     46             data = self.dataset[possibly_batched_index]
---> 47         return self.collate_fn(data)

~\Desktop\IACS\Code\Demo\utils_train.py in my_collate(batch)
    198 
    199         def my_collate(batch):
--> 200             output = [(torch.tensor(dp[0]), torch.tensor(dp[1])) for dp in batch]
    201 
    202             return output

~\Desktop\IACS\Code\Demo\utils_train.py in <listcomp>(.0)
    198 
    199         def my_collate(batch):
--> 200             output = [(torch.tensor(dp[0]), torch.tensor(dp[1])) for dp in batch]
    201 
    202             return output

TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, int64, int32, int16, int8, uint8, and bool.

I guess now the ds_train is something like
[(array([tensor([292.934]), tensor([134], dtype=torch.int32), tensor([1.1, 1.2, 1.3, 1.4, ..., 1.153]), tensor([2.1, 2.2, 2.3, 2.4, ..., 2.153]), tensor([550.123])], dtype=object), tensor([-1.3254])),
(array(...)), ...]

I’m not too sure how to fix the dtype=object issue.