The length of  one sequence is 50. All the 50 values in the sequence is part of the first time step. The next 50 values will part of the second time step.
Have slightly tweaked the Pytorch example to drive this point home
loss_fn = nn.MSELoss()

batch_size = 10
TIMESTEPS = 5

# Create some fake data
batch = torch.randn(batch_size, 50,5)
hidden = torch.zeros(batch_size, 20)
target = torch.zeros(batch_size, 10)

loss = 0
for t in range(TIMESTEPS):
    # yes! you can reuse the same network several times,
    # sum up the losses, and call backward!
    hidden, output = rnn(batch[:,:,t], hidden)
    loss += loss_fn(output, target)
loss.backward()