human
I am working on the file "Equation_Notes2.ipynb".
The first task is:
However, it would be much more convenient if the dataset implemented the iterator protocol itself, so we could simply loop over samples with for sample in dataset. 
We are losing a lot of features by using a simple for loop to iterate over the data. In particular, we are missing out on:
Batching the data
Shuffling the data
Load the data in parallel using multiprocessing workers.

Fortunately, this functionality is provided by the DataLoader class available via the torch.utils.data.DataLoader which is an iterator which provides all these features.
A DataLoader object takes a dataset object as argument and a number of options that configure the way samples are retrieved. For example, it is possible to load samples in parallel, using multiple processes. For this, the DataLoader constructor takes a num_workers argument. 
Note that DataLoaders always return batches, whose size is set with the batch_size parameter. 
List of important arguments for dataloader:
$$\begin{array}{|c|c|}
\hline
 Argument & Description \\hline
dataset \;(Dataset) & dataset\.
object \;from\.
which\.
to\.
load\.
the\.
data\\hline
batch_size (int, optional) & how \;many \;samples \;per \;batch \;to \;load (default: 1)\\hline
shuffle (bool, optional) & set \;to\.
True\.
to\.
have\.
the\.
data\.
reshuffled\.
at\.
every\.
epoch\.
(default: False).\\hline
num_workers (int, optional) &  how\.
many\.
subprocesses\.
to\.
use\.
for\.
data\.
loading.\.
0\.
means\.
that\.
the\.
data\.
will\.
be\.
loaded\.
in\.
the\.
main\.
process (default: 0)\\hline
collate_fn (callable, optional) & specify\.
how\.
exactly\.
the\.
samples\.
need\.
to\.
be\.
batched\.
to\.
form\.
a\.
mini-batch\;\\hline
\end{array}$$
Can you write Python program for it?
 ... What is a the Python program you requested?
assistant
#Here is the Python program you requested.

mydataloader = torch.utils.data.DataLoader(mydataset,batch_size=6,num_workers=2)
for i, batch in enumerate(mydataloader):
  print(i, batch)

mydataloader = torch.utils.data.DataLoader(mydataset,batch_size=6, shuffle=True, num_workers=2, drop_last=True)
for i, batch in enumerate(mydataloader):
  print(i, batch)

import math

class RangeDataset(torch.utils.data.Dataset):
  def __init__(self, start, end, step=1):
    self.start = start
    self.end = end
    self.step = step
  def __getitem__(self, index):
    value = self.start + index * self.step
    assert value < self.end
    return value
  def __len__(self):
    return math.ceil((self.end - self.start) / self.step)


dataset = RangeDataset(0, 10)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=True, num_workers=2, drop_last=True)

for i, batch in enumerate(data_loader):
  print(i, batch)