Is there a way to create a train dataset for multiple pytorch files in a folder?
I have a folder with the name processed_data.
In this file I have multiple .pt files which are named data_0.pt, data_1.pt, data_2.pt, data_3.pt, data_4.pt, data_5.pt, ....., data_998.pt, data_999.pt, data_1000.pt, data_1001.pt.
All these .pt files are representing a graph which was created using pytorch-geometric.
My question is, how do I save load all these files to create my training dataset so that I can use them in DataLoader?

A torch DataLoader needs a Dataset object. When defining your Dataset class, you need to implement __init__, __len__, and __getitem__.
__init__ is easy, but also dependent on your exact use case/context. Assuming the simplest possible situation, I'd define init to take in the data folder and a file which contains names of the training set files (one per line). Then, I'd store each file name in a list as a member of the class. So we'd have:
def __init__(self, data_folder, data_list_filename):
    self.data_folder = data_folder
    with open(data_list_filename, 'r') as f:
        self.data_file_list = f.read().splitlines()

Ok, now we have two things stored in your Dataloader: 1) the data folder and 2) a list of data filenames. That makes __len__ especially easy:
def __len__(self):
    return len(self.data_file_list)

And lastly, we just need to deal with __get_item__:
def __getitem__(self, idx):
    filename = self.data_file_list[idx]
    data, label = extract_data_from_file(filename) # this is arbitrary because I don't know how you need to do this
    return data, label

Then put all of this together under a class:
class MyDataset(Dataset):
    def __init__(self, data_folder, data_list_filename):
        self.data_folder = data_folder
        self.data_file_list = open(data_list_filename, 'r').read().splitlines()

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

    def __getitem__(self, idx):
        filename = self.data_file_list[idx]
        data, label = extract_data_from_file(filename) # idk how you plan to do this
        return data, label

Obviously, your exact use will look different. But this should get you started.
