No you can’t. But I don’t think you need to since the number of workers primarily is for when you want to fetch data from disk or perform data augmentation (or other pre-processing).
The TensorDataset is just a dataset made from tensors, without the whole torch.data.Dataset code. I made an example that you might find useful. Note that this example probably doesn’t play well for datasets that aren’t evenly divided with the batch size, like a dataset of 14, batch size of 4 -> 4+4+4=12 and then there are 2 left which isn’t enough for a full batch. Easily fixable with some if statement 
Also note that I hardcoded the number of labels to 1 on two occasions. And the more_itertools need to be pip installed
import numpy as np
import torch
from torch.utils.data import TensorDataset
import random
import more_itertools

def load_data():
  # Fake data. You can also load your images and convert them into tensors.
  number_images = 100
  images = torch.randn(number_images, 3, 2, 2)
  labels = torch.ones(number_images, 1)
  return TensorDataset(images, labels)

def get_batch(dataset, batch_idx):
  ''' Returns the data items given batch indexes '''

  # Set up the datastructures
  im_size = dataset[0][0].size()
  batch_size = len(batch_idx)
  batch_data = torch.empty((batch_size, *im_size))
  batch_labels = torch.empty((batch_size, 1))
  
  # Add data to datastructures
  for i, data_idx in enumerate(batch_idx):
    data, label = dataset[data_idx]
    batch_data[i] = data
    batch_labels[i] = label

  return batch_data, batch_labels

dataset = load_data()
data_length = len(dataset)

batch_size = 10
n_epochs = 10
for epoch in range(n_epochs):
  # Create indexes, shuffles them and split them into batches
  indexes = list(range(data_length))
  random.shuffle(indexes)
  indexes = more_itertools.chunked(indexes, batch_size)

  for batch_idx in indexes:
    images, labels = get_batch(dataset, batch_idx)
    # You can now work with your data