code: def _process_dict_column(
      X_col, categories, feature_type, min_unique_continuous
  ):
      if isinstance(X_col, np.ndarray):  # this includes ma.masked_array
          pass
      elif _pandas_installed and isinstance(X_col, pd.Series):
          return _process_pandas_column(
              X_col, categories, feature_type, min_unique_continuous
          )
      elif _pandas_installed and isinstance(X_col, pd.DataFrame):
          if X_col.shape[1] == 1:
              X_col = X_col.iloc[:, 0]
              return _process_pandas_column(
                  X_col, categories, feature_type, min_unique_continuous
              )
          elif X_col.shape[0] == 1:
              X_col = X_col.astype(
                  np.object_, copy=False
              ).values.reshape(-1)
          elif X_col.shape[1] == 0 or X_col.shape[0] == 0:
              X_col = np.empty(0, np.object_)
          else:
              msg = f"Cannot reshape to 1D. Original shape was {X_col.shape}"
              _log.error(msg)
              raise ValueError(msg)
      elif _scipy_installed and isinstance(X_col, sp.sparse.spmatrix):
          if X_col.shape[1] == 1 or X_col.shape[0] == 1:
              return _process_scipy_column(
                  X_col, categories, feature_type, min_unique_continuous
              )
          elif X_col.shape[1] == 0 or X_col.shape[0] == 0:
              X_col = np.empty(0, np.object_)
          else:
              msg = f"Cannot reshape to 1D. Original shape was {X_col.shape}"
              _log.error(msg)
              raise ValueError(msg)
      elif isinstance(X_col, list) or isinstance(X_col, tuple):
          X_col = np.array(X_col, np.object_)
      elif isinstance(X_col, str):
          # don't allow strings to get to the np.array conversion below
          X_col_tmp = np.empty(1, np.object_)
          X_col_tmp.itemset(0, X_col)
          X_col = X_col_tmp
      else:
          try:
              # we don't support iterables that get exhausted on their first examination.  This condition
              # should be detected though in preclean_X where we get the length or bin_native where we check the
              # number of samples on the 2nd run through the generator
              X_col = list(X_col)
              X_col = np.array(X_col, np.object_)
          except TypeError:
              # if our item isn't iterable, assume it has just 1 item and we'll check below if that's consistent
              X_col_tmp = np.empty(1, np.object_)
              X_col_tmp.itemset(0, X_col)
              X_col = X_col_tmp
  
      X_col = _reshape_1D_if_possible(X_col)
      return _process_numpy_column(
          X_col, categories, feature_type, min_unique_continuous
      )
  
imports: from collections import Counter
  from itertools import count
  import numpy
  import numpy.ma
  import logging
label_desc: Use image node_id 12 for calling a global function with example usage: _process_dict_column(X_col, categories, feature_type, min_unique_continuous) and returns: _process_numpy_column, _process_pandas_column, _process_pandas_column, _process_scipy_column