code: def _reshape_X(X, min_cols, n_samples, sample_source):
      if (
          X.ndim == 0 or X.shape[0] == 0
      ):  # zero dimensional arrays are possible, but really weird
          return np.empty((0, 0), X.dtype)
      elif X.ndim != 1:
          # our caller will not call this function with 2 dimensions
          # we also accept 1 dimension as below, but do not encourage it
          msg = f"X must have 2 dimensions, but has {X.ndim}"
          _log.error(msg)
          raise ValueError(msg)
  
      if n_samples is not None:
          if n_samples == 1:
              return X.reshape((1, X.shape[0]))
          elif n_samples == X.shape[0]:
              return X.reshape((n_samples, 1))
          else:
              msg = f"{sample_source} has {n_samples} samples, but X has {X.shape[0]}"
              _log.error(msg)
              raise ValueError(msg)
      elif min_cols is None or min_cols == 1:
          return X.reshape((X.shape[0], 1))
      elif min_cols <= X.shape[0]:
          return X.reshape((1, X.shape[0]))
      else:
          msg = "X is 1 dimensional"
          _log.error(msg)
          raise ValueError(msg)
  
imports: from collections import Counter
  from itertools import count
  import numpy
  import numpy.ma
  import logging
label_desc: Use image node_id 16 for calling a global function with example usage: _reshape_X(X, min_cols, n_samples, sample_source) and returns: np, X, X, X, X