Hello, what you need to do is implement your own dataset using the torch.utils.data.Dataset like this small example:
class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, inputs, labels):
        self.inputs = inputs
        self.labels = labels

    def __getitem__(self, index):
        return self.inputs[index, :], self.labels[index]

    def __len__(self):
        return len(self.input)

inputs = torch.rand(125973, 122, dtype=torch.float32)
labels = torch.rand(125973, dtype=torch.float32)
dataset = CustomDataset(inputs, labels)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)
for binputs, blabels in train_loader:
    print(binputs)
    print(blabels)

Basically, you inherit from the PyTorch Dataset class and you implement the __getitem__ and __len__ methods. __getitem__ is called to extract batch tensors and __len__ determines the number of elements in the dataset. After that, use the PyTorch DataLoader like in your example with your own dataset and everything should work fine!
The following site also shows a good example of how to use the Dataset class of PyTorch.
https://stanford.edu/~shervine/blog/pytorch-how-to-generate-data-parallel 2
Hope this helps!
(Sorry for the edit, I accidently replied half an answer)