This has been pretty odd. To actually access the correct data (t variable), I’ve had to only use the target. from the for data, target in enumerate(dataloader), data returns an integer, where target returns a tensor of [100,2] size. Is this something with how pandas handles things? The dataframe only has 2 indices, because trying to pull the target values specificially within the dataframe results in an out of bounds error.
Just for completeness here… I’m going to post my dataset method to avoid confusion.
# Data set formatting for easier input and processing by network.
class OUDataset(Dataset):

  def __init__(self, csv_file):
    self.oudataframe = pd.read_csv(csv_file)

  def __len__(self):
    return len(self.oudataframe) - 100
  
  def __getitem__(self, idx):
    data = torch.zeros(100, 1)
    target = torch.zeros(100,1)
    for i in range(0, 100):
      data[i] = torch.tensor(self.oudataframe.iloc[idx+i][0])
      target[i] = torch.tensor(self.oudataframe.iloc[idx+i][1])
    return [data, target]

######################################################################################

######################################################################################
# Function/Method definitions
######################################################################################

def main():
  k = 0 # iteration 0.
  print("Running!")
  print("Testing dataset class ...")
  dataSet = OUDataset(csv_file="../../trainingData/python/data/trainingDataOrnsteinUhlenbeck.dat")

  print("Moving to dataloader section ...")
  counter = 0 
  
  dataloader = DataLoader(dataSet, batch_size=20, shuffle=False)
  for data, target in enumerate(dataloader):
    print(data)
    print(target[0])
    print(target[1])
    break