When trying to run the example from the README, I currently get the following error

Traceback (most recent call last):
  File ".../torch_max_mem/tmp.py", line 2, in <module>
    from torch_max_mem import maximize_memory_utilization
ModuleNotFoundError: No module named 'torch_max_mem'

When I check pip list, the package name appears to be the stylized name

$ pip list | grep max
torch-max-mem     0.0.1.dev0 .../torch_max_mem/src

This PR adds a simplification for creating hashers based on the values associated to a subse of keys without having to define a lambda or named function.

The following snippet

from torch_max_mem import maximize_memory_utilization


@maximize_memory_utilization()
def func(a, *bs, batch_size: int):
    pass

raises an error

Traceback (most recent call last):
  File ".../tmp.py", line 5, in <module>
    def func(a, *bs, batch_size: int):
  File ".../venv/venv-cpu/lib/python3.8/site-packages/torch_max_mem/api.py", line 274, in __call__
    wrapped = maximize_memory_utilization_decorator(
  File ".../venv/venv-cpu/lib/python3.8/site-packages/torch_max_mem/api.py", line 150, in decorator_maximize_memory_utilization
    raise ValueError(f"{parameter_name} must be a keyword based parameter, but is {_parameter.kind}.")
ValueError: batch_size must be a keyword based parameter, but is KEYWORD_ONLY.

since _parameter.kind is KEYWORD_ONLY.

This is overly restrictive, since we only need keyword-based parameters.

Add a decorator which remembers to maximum parameter value for next time. Since this is handled internally, we do not need to expose the found parameter value to the outside, leaving the method signature unchanged.