Hello,
Usually, the splitting of training and testing data is done before using the DataLoader class of PyTorch, as the classe takes a dataset as a parameter. What you could do is separate your 65536 x 94 tensor into two tensors, one for training and the other one for testing (my rule of thumb is keep around 20% for testing). Then, you could use the PyTorch Dataset class to create you own custom dataset that would then be used in the DataLoader. Here is a small example:
class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, data):
        self.data = data

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

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

x = torch.rand(size=(65536, 94), dtype=torch.float32)
separation = int(x.shape[0] * 0.8)
train = x[:separation]
test = x[separation:]

train_dataset = CustomDataset(train)
test_dataset = CustomDataset(test)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32)

for train_sample in train_loader:
    print(train_sample.shape)
    # torch.Size([64, 94])

for test_sample in test_loader:
    print(test_sample.shape)
    # torch.Size([32, 94])

Hope this helps!