def METHOD_NAME(y, num_classes=None, dtype="float32"):
    """Converts a class vector (integers) to binary class matrix.
    E.g. for use with `categorical_crossentropy`.
    Args:
        y: Array-like with class values to be converted into a matrix
            (integers from 0 to `num_classes - 1`).
        num_classes: Total number of classes. If `None`, this would be inferred
          as `max(y) + 1`.
        dtype: The data type expected by the input. Default: `'float32'`.
    Returns:
        A binary matrix representation of the input as a NumPy array. The class
        axis is placed last.
    Example:
    >>> a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)
    >>> print(a)
    [[1. 0. 0. 0.]
     [0. 1. 0. 0.]
     [0. 0. 1. 0.]
     [0. 0. 0. 1.]]
    >>> 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.]
    """
    y = np.array(y, dtype="int")
    input_shape = y.shape
    # Shrink the last dimension if the shape is (..., 1).
    if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
        input_shape = tuple(input_shape[:-1])
    y = y.reshape(-1)
    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