code: def _feature_is_id(df, x):
      """Return Boolean if the feature column x is an ID."""
      if not _dtype_represents_categories(df[x]):
          return False
  
      category_count = df[x].value_counts().count()
      return category_count == len(df[x])
  
imports: import warnings
  import numpy
  import pandas
  from pandas.api.types import is_bool_dtype, is_categorical_dtype, is_datetime64_any_dtype, is_numeric_dtype, is_object_dtype, is_string_dtype, is_timedelta64_dtype
  from sklearn import preprocessing, tree
  from sklearn.metrics import f1_score, mean_absolute_error
  from sklearn.model_selection import cross_val_score
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 8 for calling a global function with example usage: _feature_is_id(df, x) and returns: unknown, False