code: def _apply_transforms(self, element, ranges, style):
      new_style = dict(style)
      for k, v in style.items():
          if isinstance(v, str):
              if validate(k, v) == True:
                  continue
              elif v in element or (
                  isinstance(element, Graph) and v in element.nodes
              ):
                  v = dim(v)
              elif any(d == v for d in self.overlay_dims):
                  v = dim(next(d for d in self.overlay_dims if d == v))
  
          if not isinstance(v, dim):
              continue
          elif (
              not v.applies(element)
              and v.dimension not in self.overlay_dims
          ):
              new_style.pop(k)
              self.param.warning(
                  f"Specified {k} dim transform {v!r} could not be "
                  "applied, as not all dimensions could be resolved."
              )
              continue
  
          if v.dimension in self.overlay_dims:
              ds = Dataset(
                  {d.name: v for d, v in self.overlay_dims.items()},
                  list(self.overlay_dims),
              )
              val = v.apply(ds, ranges=ranges, flat=True)[0]
          elif type(element) is Path:
              val = np.concatenate(
                  [
                      v.apply(el, ranges=ranges, flat=True)
                      for el in element.split()
                  ]
              )
          else:
              val = v.apply(element, ranges)
  
          if (
              not np.isscalar(val)
              and len(util.unique_array(val)) == 1
              and ("color" not in k or validate("color", val))
          ):
              val = val[0]
  
          if not np.isscalar(val) and k in self._nonvectorized_styles:
              element = type(element).__name__
              raise ValueError(
                  f'Mapping a dimension to the "{k}" '
                  "style option is not supported by the "
                  f"{element} element using the {self.renderer.backend} "
                  f'backend. To map the "{v.dimension}" dimension '
                  f"to the {k} use a groupby operation "
                  "to overlay your data along the dimension."
              )
  
          style_groups = getattr(self, "_style_groups", [])
          groups = [sg for sg in style_groups if k.startswith(sg)]
          group = groups[0] if groups else None
          prefix = "" if group is None else group + "_"
          if (
              k in (prefix + "c", prefix + "color")
              and isinstance(val, util.arraylike_types)
              and not validate("color", val)
          ):
              new_style.pop(k)
              self._norm_kwargs(
                  element, ranges, new_style, v, val, prefix
              )
              if val.dtype.kind in "OSUM":
                  range_key = dim_range_key(v)
                  if (
                      range_key in ranges
                      and "factors" in ranges[range_key]
                  ):
                      factors = ranges[range_key]["factors"]
                  else:
                      factors = util.unique_array(val)
                  val = util.search_indices(val, factors)
                  labels = getattr(self, "legend_labels", {})
                  factors = [labels.get(f, f) for f in factors]
                  new_style["cat_legend"] = {
                      "title": v.dimension,
                      "prop": "c",
                      "factors": factors,
                  }
              k = prefix + "c"
          new_style[k] = val
  
      for k, val in list(new_style.items()):
          # If mapped to color/alpha override static fill/line style
          if k == "c":
              new_style.pop("color", None)
  
          style_groups = getattr(self, "_style_groups", [])
          groups = [sg for sg in style_groups if k.startswith(sg)]
          group = groups[0] if groups else None
          prefix = "" if group is None else group + "_"
  
          # Check if element supports fill and line style
          supports_fill = (
              prefix != "edge" or getattr(self, "filled", True)
          ) and any(
              o.startswith(prefix + "face") for o in self.style_opts
          )
  
          if k in (prefix + "c", prefix + "color") and isinstance(
              val, util.arraylike_types
          ):
              fill_style = new_style.get(prefix + "facecolor")
              if fill_style and validate("color", fill_style):
                  new_style.pop("facecolor")
  
              line_style = new_style.get(prefix + "edgecolor")
  
              # If glyph has fill and line style is set overriding line color
              if supports_fill and line_style is not None:
                  continue
  
              if line_style and validate("color", line_style):
                  new_style.pop("edgecolor")
          elif k == "facecolors" and not isinstance(
              new_style.get("color", new_style.get("c")), np.ndarray
          ):
              # Color overrides facecolors if defined
              new_style.pop("color", None)
              new_style.pop("c", None)
  
      return new_style
  
imports: import copy
  import math
  import warnings
  from types import FunctionType
  import matplotlib.colors
  import numpy
  import param
  from matplotlib import ticker
  from matplotlib.dates import date2num
  from matplotlib.image import AxesImage
  from packaging.version import Version
  from ...core import CompositeOverlay, Dataset, DynamicMap, Element, Element3D, NdOverlay, util
  from ...core.options import Keywords, abbreviated_exception
  from ...element import Graph, Path
  from ...streams import Stream
  from ...util.transform import dim
  from ..plot import GenericElementPlot, GenericOverlayPlot
  from ..util import color_intervals, dim_range_key, process_cmap
  from .plot import MPLPlot, mpl_rc_context
  from .util import EqHistNormalize, mpl_version, validate, wrap_formatter
label_desc: Use image node_id 20 for calling the ElementPlot obj's underlying member method code with example usage: obj._apply_transforms(element, ranges, style) and returns: new_style