code: def one_hot(y, num_labels="auto", dtype="float"):
      """One-hot encoding of class labels
  
      Parameters
      ----------
      y : array-like, shape = [n_classlabels]
          Python list or numpy array consisting of class labels.
      num_labels : int or 'auto'
          Number of unique labels in the class label array. Infers the number
          of unique labels from the input array if set to 'auto'.
      dtype : str
          NumPy array type (float, float32, float64) of the output array.
  
      Returns
      ----------
      ary : numpy.ndarray, shape = [n_classlabels]
          One-hot encoded array, where each sample is represented as
          a row vector in the returned array.
  
      Examples
      ----------
      For usage examples, please see
      https://rasbt.github.io/mlxtend/user_guide/preprocessing/one_hot/
  
      """
      if not (num_labels == "auto" or isinstance(num_labels, int)):
          raise AttributeError(
              'num_labels must be an integer or "auto"'
          )
      if isinstance(y, list):
          yt = np.asarray(y)
      else:
          yt = y
      if not len(yt.shape) == 1:
          raise AttributeError("y array must be 1-dimensional")
      if num_labels == "auto":
          # uniq = np.unique(yt).shape[0]
          uniq = np.max(yt + 1)
      else:
          uniq = num_labels
      if uniq == 1:
          ary = np.array([[0.0]], dtype=dtype)
  
      else:
          ary = np.zeros((len(y), uniq))
          for i, val in enumerate(y):
              ary[i, val] = 1
  
      return ary.astype(dtype)
  
label_desc: Use image node_id 1 for calling a global function with example usage: one_hot(y, num_labels, dtype) and returns: ary