code: def clean_x_axis_non_existent_values(x_axis, distribution):
      """Remove values from x_axis where the distribution has no values."""
      # Find the index of the first value in x_axis that is bigger than the value in distribution
      ixs = np.searchsorted(sorted(distribution), x_axis, side="left")
      # If 2 neighboring indexes are the same, it means that there are no values in the distribution for
      # the corresponding value in x_axis. We remove it.
      x_axis = [
          x_axis[i] for i in range(len(ixs)) if ixs[i] != ixs[i - 1]
      ]
      return x_axis
  
file_path: deepchecks/deepchecks/nlp/utils/nlp_plot.py
imports: from typing import List, Sequence
  import numpy
  import pandas
  import plotly.express
  import plotly.graph_objs
  from deepchecks.nlp import TextData
  from deepchecks.nlp.task_type import TaskType
  from deepchecks.nlp.utils.text import break_to_lines_and_trim
  from deepchecks.nlp.utils.text_properties import TEXT_PROPERTIES_DESCRIPTION
  from deepchecks.nlp.utils.token_classification_utils import annotated_token_classification_text, count_token_classification_labels
  from deepchecks.utils.dataframes import un_numpy
  from deepchecks.utils.distribution.plot import get_density
  from deepchecks.utils.plot import DEFAULT_DATASET_NAMES, colors, common_and_outlier_colors
  from deepchecks.utils.strings import get_docs_link
label_desc: Use image node_id 1 for calling a global function with example usage: clean_x_axis_non_existent_values(x_axis, distribution) and returns: x_axis