code: def _generate_table_trace(
      n_samples,
      annotation_ratio,
      categorical_metadata,
      numerical_metadata,
      categorical_properties,
      numerical_properties,
  ):
      data_cell = [
          "<b>Number of samples</b>",
          "<b>Annotation ratio</b>",
          "<b>Metadata categorical columns</b>",
          "<b>Metadata numerical columns</b>",
          "<b>Categorical properties</b>",
          "<b>Numerical properties</b>",
      ]
  
      info_cell = _get_table_row_data(
          n_samples=n_samples,
          annotation_ratio=annotation_ratio,
          categorical_metadata=categorical_metadata,
          numerical_metadata=numerical_metadata,
          categorical_properties=categorical_properties,
          numerical_properties=numerical_properties,
          max_values_to_show=7,
      )
  
      trace = go.Table(
          header={"fill": {"color": "white"}},
          cells={
              "values": [data_cell, info_cell],
              "align": ["left"],
              "font_size": 12,
              "height": 30,
          },
      )
      return trace
  
file_path: deepchecks/deepchecks/nlp/utils/text_data_plot.py
imports: from typing import List, Optional
  import numpy
  import pandas
  import plotly.graph_objs
  from plotly.subplots import make_subplots
  from deepchecks.nlp.task_type import TaskType, TTextLabel
  from deepchecks.nlp.utils.text import break_to_lines_and_trim
  from deepchecks.nlp.utils.text_properties import TEXT_PROPERTIES_DESCRIPTION
  from deepchecks.utils.dataframes import un_numpy
  from deepchecks.utils.distribution.plot import get_density
  from deepchecks.utils.plot import feature_distribution_colors
  from deepchecks.utils.strings import format_percent, get_docs_link
label_desc: Use image node_id 3 for calling a global function with example usage: _generate_table_trace(n_samples, annotation_ratio, categorical_metadata, numerical_metadata, categorical_properties, numerical_properties) and returns: trace