class_docstr: Evaluation result for one metric.
  Stores all ExecutionCases with specified metric scores
  Computes human-friendly tables with results and some plots
class_docstr_tok: ["Evaluation","result","for","one","metric",".","Stores","all","ExecutionCases","with","specified","metric","scores","Computes","human-friendly","tables","with","results","and","some","plots"]
docstr: :param fold: FoldId to plot
  :param offset: first iteration to plot
  :return: plotly figure for all cases on specified fold
docstr_tok: [":","param","fold",":","FoldId","to","plot",":","param","offset",":","first","iteration","to","plot",":","return",":","plotly","figure","for","all","cases","on","specified","fold"]
code: def create_fold_learning_curves(self, fold, offset=None):
      """
  
      :param fold: FoldId to plot
      :param offset: first iteration to plot
      :return: plotly figure for all cases on specified fold
      """
      import plotly.graph_objs as go
  
      traces = []
      for case in self.get_cases():
          case_result = self.get_case_result(case)
          scores_curve = case_result.get_fold_curve(fold)
          if offset is not None:
              first_idx = offset
          else:
              first_idx = int(len(scores_curve) * 0.1)
  
          traces.append(
              go.Scatter(
                  x=[
                      i * int(case_result.get_eval_step())
                      for i in range(first_idx, len(scores_curve))
                  ],
                  y=scores_curve[first_idx:],
                  mode="lines",
                  name="Case {}".format(case),
              )
          )
  
      layout = go.Layout(
          title="Learning curves for metric {} on fold #{}".format(
              self._metric_description, fold
          ),
          hovermode="closest",
          xaxis=dict(
              title="Iteration",
              ticklen=5,
              zeroline=False,
              gridwidth=2,
          ),
          yaxis=dict(
              title="Metric",
              ticklen=5,
              gridwidth=2,
          ),
          showlegend=True,
      )
      fig = go.Figure(data=traces, layout=layout)
      return fig
  
imports: import numpy
  import pandas
  from enum import Enum
  from ..None import CatBoostError
  from ..core import metric_description_or_str_to_str
  from ..utils import compute_wx_test
label_desc: Use image node_id 14 for calling the MetricEvaluationResult obj's underlying member method code with example usage: obj.create_fold_learning_curves(fold, offset) and returns: fig