docstr: Create a scale
docstr_tok: ["Create","a","scale"]
code: def get_scale(self, plot):
      """
      Create a scale
      """
      # This method does some introspection to save users from
      # scale mismatch error. This could happen when the
      # aesthetic is mapped to a categorical but the limits
      # are not provided in categorical form. We only handle
      # the case where the mapping uses an expression to
      # conver to categorical e.g `aes(color='factor(cyl)')`.
      # However if `'cyl'` column is a categorical and the
      # mapping is `aes(color='cyl')`, that will result in
      # an error. If later case proves common enough then we
      # could inspect the data and be clever based on that too!!
      ae = self.aesthetic
      series = self.limits_series
      ae_values = []
  
      # Look through all the mappings for this aesthetic,
      # if we detect any factor stuff then we convert the
      # limits data to categorical so that the right scale
      # can be choosen. This should take care of the most
      # common use cases.
      for layer in plot.layers:
          with suppress(KeyError):
              value = layer.mapping[ae]
              if isinstance(value, str):
                  ae_values.append(value)
  
      for value in ae_values:
          if "factor(" in value or "Categorical(" in value:
              series = pd.Categorical(self.limits_series)
              break
      return make_scale(
          self.aesthetic, series, limits=self.limits, trans=self.trans
      )
  
imports: import sys
  from contextlib import suppress
  import pandas
  from .._utils import array_kind
  from ..exceptions import PlotnineError
  from ..geoms import geom_blank
  from ..mapping.aes import ALL_AESTHETICS, aes
  from ..scales.scales import make_scale
label_desc: Use image node_id 2 for calling the _lim obj's underlying member method code with example usage: obj.get_scale(plot) and returns: make_scale