code: def to_categorical(y, num_classes=None, dtype="float32"):
      """Converts a class vector (integers) to binary class matrix.
  
      E.g. for use with categorical_crossentropy.
  
      Args:
          y: class vector to be converted into a matrix
              (integers from 0 to num_classes).
          num_classes: total number of classes. If `None`, this would be inferred
            as the (largest number in `y`) + 1.
          dtype: The data type expected by the input. Default: `'float32'`.
  
      Returns:
          A binary matrix representation of the input. The classes axis is placed
          last.
  
      Example:
  
      >>> a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)
      >>> a = tf.constant(a, shape=[4, 4])
      >>> print(a)
      tf.Tensor(
        [[1. 0. 0. 0.]
         [0. 1. 0. 0.]
         [0. 0. 1. 0.]
         [0. 0. 0. 1.]], shape=(4, 4), dtype=float32)
  
      >>> b = tf.constant([.9, .04, .03, .03,
      ...                  .3, .45, .15, .13,
      ...                  .04, .01, .94, .05,
      ...                  .12, .21, .5, .17],
      ...                 shape=[4, 4])
      >>> loss = tf.keras.backend.categorical_crossentropy(a, b)
      >>> print(np.around(loss, 5))
      [0.10536 0.82807 0.1011  1.77196]
  
      >>> loss = tf.keras.backend.categorical_crossentropy(a, a)
      >>> print(np.around(loss, 5))
      [0. 0. 0. 0.]
  
      Raises:
          Value Error: If input contains string value
  
      """
      y = np.array(y, dtype="int")
      input_shape = y.shape
      if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
          input_shape = tuple(input_shape[:-1])
      y = y.ravel()
      if not num_classes:
          num_classes = np.max(y) + 1
      n = y.shape[0]
      categorical = np.zeros((n, num_classes), dtype=dtype)
      categorical[np.arange(n), y] = 1
      output_shape = input_shape + (num_classes,)
      categorical = np.reshape(categorical, output_shape)
      return categorical
  
label_desc: Use image node_id 1 for calling a global function with example usage: to_categorical(y, num_classes, dtype) and returns: categorical