code: def validate_columns_exist(
      df: pd.DataFrame,
      columns: t.Union[Hashable, t.List[Hashable]],
      raise_error: bool = True,
  ) -> bool:
      """Validate given columns exist in dataframe.
  
      Parameters
      ----------
      df : pd.DataFrame
          dataframe to inspect
      columns : t.Union[Hashable, t.List[Hashable]]
          Column names to check
      raise_error : bool, default: True
          whether to raise an error if some column is not present in the dataframe or not
  
      Raises
      ------
      DeepchecksValueError
          If some of the columns do not exist within provided dataframe.
          If receives empty list of 'columns'.
          If not all elements within 'columns' list are hashable.
      """
      error_message = "columns - expected to receive not empty list of hashable values!"
      columns = ensure_hashable_or_mutable_sequence(
          columns, message=error_message
      )
  
      is_empty = len(columns) == 0
  
      if raise_error and is_empty:
          raise DeepchecksValueError(error_message)
      elif not raise_error and is_empty:
          return False
  
      difference = set(columns) - set(df.columns)
      all_columns_present = len(difference) == 0
  
      if raise_error and not all_columns_present:
          stringified_columns = ",".join(map(str, difference))
          raise DeepchecksValueError(
              f"Given columns do not exist in dataset: {stringified_columns}"
          )
  
      return all_columns_present
  
file_path: deepchecks/deepchecks/utils/dataframes.py
imports: import typing
  import numpy
  import pandas
  from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype, is_numeric_dtype
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.utils.type_inference import infer_categorical_features
  from deepchecks.utils.typing import Hashable
  from deepchecks.utils.validation import ensure_hashable_or_mutable_sequence
label_desc: Use image node_id 6 for calling a global function with example usage: validate_columns_exist(df, columns, raise_error) and returns: all_columns_present, False