class_docstr: Block with Granger causality matrix
class_docstr_tok: ["Block","with","Granger","causality","matrix"]
code: def _plot(
      self, names: List[str], critical: float
  ) -> Tuple[go.Figure, Any]:
      if not names:
          return (
              EmptyFigure(height=self._height, text="Select a series"),
              "",
          )
      if self.dfp.empty:
          return EmptyFigure(height=self._height, text="No data"), ""
  
      dfp, dfl, granger_diff = self.dfp, self.dfl, self.granger_diff
      names_x, names_y = (
          [f"{c}[{s}]" for c in names] for s in ["x", "y"]
      )
      dfp = dfp.loc[names_y, names_x]
      dfl = dfl.loc[names_y, names_x]
      text = ["Critical value ", html.Code(critical), ". "]
      if granger_diff > 0:
          text += [
              "Data differenced ",
              html.Code(f"{granger_diff}"),
              " time(s) to try to achieve stationarity.",
          ]
      return (
          viz.GrangerMatrix(
              dfp, dfl, critical, height=self._height, xrotation=45
          ).plot,
          text,
      )
  
imports: from typing import List, Tuple, Callable, Any
  import pandas
  from tslumen.plot import interactive
  from tslumen.plot.interactive.base import go
  from tslumen.report.dashboard._dash import dbc, html, Component, Plot, EmptyFigure
  from tslumen.report.dashboard.base import DashInput, DashSection, DashBlock
label_desc: Use image node_id 4 for calling the BlockTSGranger obj's underlying member method code with example usage: obj._plot(names, critical) and returns: viz,  text, EmptyFigure,  str, EmptyFigure,  str