code: def is_unique(data: np.ndarray, *, axis: int = -1) -> np.ndarray:
      """Determine if the input array consists of all unique values
      along a given axis.
  
      Parameters
      ----------
      data : np.ndarray
          The input array
      axis : int
          The target axis
  
      Returns
      -------
      is_unique
          Array of booleans indicating whether the data is unique along the chosen
          axis.
          This array will have one fewer dimension than the input.
  
      See Also
      --------
      count_unique
  
      Examples
      --------
      >>> x = np.vander(np.arange(5))
      >>> x
      array([[  0,   0,   0,   0,   1],
         [  1,   1,   1,   1,   1],
         [ 16,   8,   4,   2,   1],
         [ 81,  27,   9,   3,   1],
         [256,  64,  16,   4,   1]])
      >>> # Check uniqueness along rows
      >>> librosa.util.is_unique(x, axis=0)
      array([ True,  True,  True,  True, False])
      >>> # Check uniqueness along columns
      >>> librosa.util.is_unique(x, axis=-1)
      array([False, False,  True,  True,  True])
      """
      return np.apply_along_axis(__is_unique, axis, data)
  
imports: from __future__ import annotations
  import scipy.ndimage
  import scipy.sparse
  import numpy
  import numba
  from numpy.lib.stride_tricks import as_strided
  from .._cache import cache
  from .exceptions import ParameterError
  from .deprecation import Deprecated
  from numpy.typing import ArrayLike, DTypeLike
  from typing import Any, Callable, Iterable, List, Dict, Optional, Sequence, Tuple, TypeVar, Union, overload
  from typing_extensions import Literal
  from .._typing import _SequenceLike, _FloatLike_co, _ComplexLike_co
label_desc: Use image node_id 40 for calling a global function with example usage: is_unique(data) and returns: np