Consider creating a mechanism for submitting multiple times to the same hybrid sampler

For example, if you wanted to submit multiple times to the hybrid NL solver without reuploading the model between runs, you could do so with something like
```python
import numpy as np

from dwave.cloud.computation import Future
from dwave.optimization.generators import knapsack
from dwave.system import LeapHybridNLSampler

model = knapsack(range(100), range(100), capacity=100)
x, = model.iter_decisions()

sampler = LeapHybridNLSampler()

# Get the timelimit for the problem
time_limit = sampler.estimated_min_time_limit(model)

# We can upload at most one state
problem_data_id = sampler.solver.upload_nlm(model, max_num_states=1).result()

def hook(model, future):
model.states.from_file(future.answer_data, check_header=False)

# 10 submissions
futures = []
for _ in range(10):
futures.append(sampler.solver.sample_nlm(problem_data_id, time_limit=time_limit))

states = []
for completed in Future.as_completed(futures):
model.states.from_future(completed, hook)

# This needs to be saved "off" the model! It will be overwritten in the
# next loop
print(x.state())

states.extend(np.asarray(x.state(i)) for i in range(len(model.states)))

print(states)
```
This seems to be a semi-common use case so bundling it into a method or function might be helpful.

Probably needs https://github.com/dwavesystems/dwave-optimization/issues/185 in the case of NL to be done elegantly. For CQM/BQM/DQM we could use [dimod.concatenate()](https://docs.ocean.dwavesys.com/en/stable/docs_dimod/reference/generated/dimod.concatenate.html).