class_docstr: Check if there are columns which have only a single unique value in all rows.
  
  Parameters
  ----------
  columns : Union[Hashable, List[Hashable]] , default: None
      Columns to check, if none are given checks all
      columns except ignored ones.
  ignore_columns : Union[Hashable, List[Hashable]] , default: None
      Columns to ignore, if none given checks based
      on columns variable.
  ignore_nan : bool, default True
      Whether to ignore NaN values in a column when counting the number of unique values.
  n_samples : int , default: 10_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
docstr: Run check.
  
  Returns
  -------
  CheckResult
      value of result is a dict of all columns with number of unique values in format {column: number_of_uniques}
      display is a series with columns that have only one unique
code: def run_logic(self, context: Context, dataset_kind) -> CheckResult:
      """Run check.
  
      Returns
      -------
      CheckResult
          value of result is a dict of all columns with number of unique values in format {column: number_of_uniques}
          display is a series with columns that have only one unique
      """
      # Validate parameters
      df = (
          context.get_data_by_kind(dataset_kind)
          .sample(self.n_samples, random_state=self.random_state)
          .data
      )
      df = select_from_dataframe(df, self.columns, self.ignore_columns)
  
      num_unique_per_col = df.nunique(dropna=self.ignore_nan)
      is_single_unique_value = num_unique_per_col == 1
  
      if context.with_display and is_single_unique_value.any():
          # get names of columns with one unique value
          # pylint: disable=unsubscriptable-object
          cols_with_single = is_single_unique_value[
              is_single_unique_value
          ].index.to_list()
          uniques = pd.DataFrame(
              {
                  column_name: [
                      column.sort_values(kind="mergesort").values[0]
                  ]
                  for column_name, column in df.loc[
                      :, cols_with_single
                  ].items()
              }
          )
          uniques.index = ["Single unique value"]
          display = [
              "The following columns have only one unique value",
              uniques,
          ]
      else:
          display = None
  
      return CheckResult(
          num_unique_per_col.to_dict(),
          header="Single Value in Column",
          display=display,
      )
  
file_path: deepchecks/deepchecks/tabular/checks/data_integrity/is_single_value.py
imports: from typing import List, Union
  import pandas
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.tabular.utils.messages import get_condition_passed_message
  from deepchecks.utils.dataframes import select_from_dataframe
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 2 for calling the IsSingleValue obj's underlying member method code with example usage: obj.run_logic(context, dataset_kind) and returns: CheckResult