class_docstr: Wrapper for Python dict that makes it sliceable across values.
  
  Use this if your input data is a dictionary and you have problems
  with sklearn not being able to slice it. Wrap your dict with
  SliceDict and it should usually work.
  
  Note:
  
  * SliceDict cannot be indexed by integers, if you want one row,
    say row 3, use `[3:4]`.
  * SliceDict accepts numpy arrays and torch tensors as values.
  
  Examples
  --------
  >>> X = {'key0': val0, 'key1': val1}
  >>> search = GridSearchCV(net, params, ...)
  >>> search.fit(X, y)  # raises error
  >>> Xs = SliceDict(key0=val0, key1=val1)  # or Xs = SliceDict(**X)
  >>> search.fit(Xs, y)  # works
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code: def copy(self):
      return type(self)(**self)
  
imports: from collections.abc import Sequence
  from functools import partial
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.base import TransformerMixin
  import torch
  from skorch._doctor import SkorchDoctor
  from skorch.cli import parse_args
  from skorch.utils import _make_split
  from skorch.utils import to_numpy
  from skorch.utils import is_torch_data_type
  from skorch.utils import to_tensor
label_desc: Use image node_id 8 for calling the SliceDict obj's underlying member method code with example usage: obj.copy() and returns: type