Why:

The algorithms will be working with trial objects instead of tuple of
values. This means the space needs to sample trials, and space
transformations should be applied on trials as well instead of tuples of
values.

How:

For simplicity, only interface of the space classes (TransformedSpace
and ReshapedSpace) will be working with trials. The transformations per
dimension will be applied using tuple of values so that, in particular,
reshaping operations remain straightforward.

To facilitate debugging, transformed trials are wrapped so that the
original trial can still be accessible. This will prove handy in
algorithms if we need access to original trial objects, and also because
the ID of the transformed trial should always be based on the original
parameters (otherwise the ID gets incoherent with the database).

The use of `__getattr__` to copy the TransformedTrial was causing an
infinite recursion error.

Why:

If the names of the dimensions have the same prefix, but some have a
shape, a transformed space with have a different ordering of dimension.
For example, the following dimensions will have their named sorted
differently:

  dim (shape 2), dim1 (no shape) -->  dim1, dim[0], dim[1]

This is causing an issue when we try to restore the shape of the
transformed dimension, with the names being swapped.

How:

When restoring shape, keep track of the original keys and their order,
and reassign the restored dimensions to the correct index (correct dim
name).

Reshaped transform on dim:

Sometimes we need to apply a transformation on a dimension only. So far
the transformation of View could only be applied on a full point,
because the ReshapedDimension wrapping the view was expected to fetch
the corresponding dimension at self.index. This commit pushes up the
fetch at self.index at the level of the ReshapedSpace instead, the only
place where we are expected to work on a full point, not only on a
dimension.

default value:

Transformed dimensions should support default value like original
dimensions.

Why:

The branching method will be used by algorithm generating new trials
based on existing one, such as Hyperband increasing the fidelity, and
later on Population Based Training which could also change the working
directory.

How:

When calling branch(), a new trial is created with same parameters as
current one, except for the parameter values passed to
(ex: ``branch(params={'some': 'values'})``).

Why:

The wrapper makes it cumbersome to keep a coherent interface with the
base Trial object. Also it does not bring much value beside providing
access to the original trial and its corresponding id. Algorithms can
revert the trial to access the trial id.

How:

Return a trial object with params overwritten instead. Like it was
already done when reverting a transformed trial anyway. Now base
algo.get_id() method compute the trial hash based on the reverted trial.

Why:

Deprecated and to be removed for v0.2.0. The newest versions
regroup_dims and flatten_dims are also useless now due to the flattened
space. They are also removed.

Why:

When flattening the space, dims of shape (1, ) should be flattened
as well otherwise the parameters will be a list of one element.

Why:

The deepcopy is failing on github-actions with error
`RuntimeError: dictionary changed size during iteration`. I have been
unable to reproduce the issue locally both with python 3.6 and 3.7. It
does fail on 3.7 on github-actions. Taking a copy of the dictionary to
do the deep copy should fix the issue only a dictionary inside some
trials is the source of the issue. The stack trace seams to hint towards
trials_info as the culprit however.

```
 tests/unittests/benchmark/test_benchmark_client.py:345:
  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
  src/orion/benchmark/__init__.py:90: in process
      study.execute(n_workers)
  src/orion/benchmark/__init__.py:341: in execute
      experiment.workon(self.task, n_workers=n_workers, max_trials=max_trials)
  src/orion/client/experiment.py:767: in workon
      for _ in range(n_workers)
  src/orion/executor/joblib_backend.py:32: in wait
      return joblib.Parallel(n_jobs=self.n_workers)(futures)
  .tox/py/lib/python3.7/site-packages/joblib/parallel.py:1056: in __call__
      self.retrieve()
  .tox/py/lib/python3.7/site-packages/joblib/parallel.py:935: in retrieve
      self._output.extend(job.get(timeout=self.timeout))
  /opt/hostedtoolcache/Python/3.7.12/x64/lib/python3.7/multiprocessing/pool.py:657: in get
      raise self._value
  /opt/hostedtoolcache/Python/3.7.12/x64/lib/python3.7/multiprocessing/pool.py:121: in worker
      result = (True, func(*args, **kwds))
  .tox/py/lib/python3.7/site-packages/joblib/_parallel_backends.py:595: in __call__
      return self.func(*args, **kwargs)
  .tox/py/lib/python3.7/site-packages/joblib/parallel.py:263: in __call__
      for func, args, kwargs in self.items]
  .tox/py/lib/python3.7/site-packages/joblib/parallel.py:263: in <listcomp>
      for func, args, kwargs in self.items]
  src/orion/client/experiment.py:781: in _optimize
      with self.suggest(pool_size=pool_size) as trial:
  src/orion/client/experiment.py:560: in suggest
      trial = reserve_trial(self._experiment, self._producer, pool_size)
  src/orion/client/experiment.py:54: in reserve_trial
      producer.produce(pool_size)
  src/orion/core/worker/producer.py:115: in produce
      self.algorithm.set_state(self.naive_algorithm.state_dict)
  src/orion/core/worker/primary_algo.py:47: in state_dict
      return self.algorithm.state_dict
  src/orion/algo/tpe.py:265: in state_dict
      _state_dict = super(TPE, self).state_dict
  src/orion/algo/base.py:132: in state_dict
      return {"_trials_info": copy.deepcopy(self._trials_info)}
  /opt/hostedtoolcache/Python/3.7.12/x64/lib/python3.7/copy.py:150: in deepcopy
      y = copier(x, memo)
  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

  x = {'0ef99dad51485ac9518b49c19b43f4ec': (Trial(experiment=2, status='completed', params=x[0]:0.5497,x[1]:2.397,x[2]:-4.29...params=x[0]:-0.8816,x[1]:2.087,x[2]:1.176), {'constraint': [], 'gradient': None, 'objective': 1187.240632192948}), ...}
  memo = {140575410255504: datetime.datetime(2021, 11, 11, 22, 57, 32, 364584), 140575419437584: datetime.datetime(2021, 11, 11....datetime(2021, 11, 11, 22, 57, 32, 177829), 140575419438016: datetime.datetime(2021, 11, 11, 22, 57, 32, 241378), ...}
  deepcopy = <function deepcopy at 0x7fdac6dfb680>

      def _deepcopy_dict(x, memo, deepcopy=deepcopy):
          y = {}
          memo[id(x)] = y
  >       for key, value in x.items():
  E       RuntimeError: dictionary changed size during iteration
```