code: def _modin_compute_data_summary(data):
      """Perform computation for summary statistics and data description.
  
      Args:
          data: The dataframe
  
      Raises:
          ValueError: Invalid input data type.
  
      Returns:
          The Modin dataframe with metrics in rows
      """
      if _is_series(data):
          data = _compat["modin.pandas"].DataFrame(data)
  
      if not _is_dataframe(data):
          raise ValueError("Data must be a Modin DataFrame")
  
      info_data = pd.DataFrame(
          {
              "Info": [
                  data.shape[0],
                  data.shape[1],
                  _sizeof_fmt(data.memory_usage().sum(), ""),
              ]
          },
          index=["Rows", "Columns", "Size in Memory"],
      )
  
      # Save column order
      columns = data.columns
  
      dtypes = data.dtypes.to_numpy()
      s_mean = data.mean(numeric_only=True).reindex().to_numpy()
      s_sd = data.std(numeric_only=True).reindex(columns).to_numpy()
      s_med = data.median(numeric_only=True).reindex(columns).to_numpy()
      s_min = data.min(numeric_only=True).reindex(columns).to_numpy()
      s_max = data.max(numeric_only=True).reindex(columns).to_numpy()
      s_zero = data[data == 0].fillna(0).sum().astype(int).to_numpy()
      s_null = data.isnull().sum().astype(int).to_numpy()
      s_unique = data.nunique().to_numpy()
      s_freq = (
          data.apply(lambda x: mode1(x.astype("str")))
          .iloc[0,]
          .to_numpy()
      )
  
      summary_data = pd.DataFrame(
          np.vstack(
              [
                  dtypes,
                  s_null,
                  s_zero,
                  s_min,
                  s_med,
                  s_max,
                  s_mean,
                  s_sd,
                  s_unique,
                  s_freq,
              ]
          ).transpose(),
          columns=[
              "Data Type",
              "Nulls",
              "Zeros",
              "Min",
              "Median",
              "Max",
              "Mean",
              "Standard Deviation",
              "Unique",
              "Top Frequency",
          ],
          index=columns,
      )
  
      return SummaryWidget(data, info_data, summary_data)
  
imports: from typing import Dict, Callable, Optional
  import warnings
  import pandas
  from pandas.core.dtypes.common import is_float
  import numpy
  from data_describe._widget import BaseWidget
  from data_describe.compat import _requires, _compat, _is_series, _is_dataframe
  from data_describe.backends._backends import _get_compute_backend
label_desc: Use image node_id 4 for calling a global function with example usage: _modin_compute_data_summary(data) and returns: SummaryWidget