code: def dst(
      x,
      type=2,
      n=None,
      axis=-1,
      norm=None,
      overwrite_x=False,
      workers=None,
  ):
      r"""Return the Discrete Sine Transform of arbitrary type sequence x.
  
      Parameters
      ----------
      x : array_like
          The input array.
      type : {1, 2, 3, 4}, optional
          Type of the DST (see Notes). Default type is 2.
      n : int, optional
          Length of the transform. If ``n < x.shape[axis]``, `x` is
          truncated.  If ``n > x.shape[axis]``, `x` is zero-padded. The
          default results in ``n = x.shape[axis]``.
      axis : int, optional
          Axis along which the dst is computed; the default is over the
          last axis (i.e., ``axis=-1``).
      norm : {"backward", "ortho", "forward"}, optional
          Normalization mode (see Notes). Default is "backward".
      overwrite_x : bool, optional
          If True, the contents of `x` can be destroyed; the default is False.
      workers : int, optional
          Maximum number of workers to use for parallel computation. If negative,
          the value wraps around from ``os.cpu_count()``.
          See :func:`~scipy.fft.fft` for more details.
  
      Returns
      -------
      dst : ndarray of reals
          The transformed input array.
  
      See Also
      --------
      idst : Inverse DST
  
      Notes
      -----
      For a single dimension array ``x``.
  
      For ``norm="backward"``, there is no scaling on the `dst` and the `idst` is
      scaled by ``1/N`` where ``N`` is the "logical" size of the DST. For
      ``norm='ortho'`` both directions are scaled by the same factor
      ``1/sqrt(N)``.
  
      There are, theoretically, 8 types of the DST for different combinations of
      even/odd boundary conditions and boundary off sets [1]_, only the first
      4 types are implemented in SciPy.
  
      **Type I**
  
      There are several definitions of the DST-I; we use the following for
      ``norm="backward"``. DST-I assumes the input is odd around :math:`n=-1` and
      :math:`n=N`.
  
      .. math::
  
          y_k = 2 \sum_{n=0}^{N-1} x_n \sin\left(\frac{\pi(k+1)(n+1)}{N+1}\right)
  
      Note that the DST-I is only supported for input size > 1.
      The (unnormalized) DST-I is its own inverse, up to a factor :math:`2(N+1)`.
      The orthonormalized DST-I is exactly its own inverse.
  
      **Type II**
  
      There are several definitions of the DST-II; we use the following for
      ``norm="backward"``. DST-II assumes the input is odd around :math:`n=-1/2` and
      :math:`n=N-1/2`; the output is odd around :math:`k=-1` and even around :math:`k=N-1`
  
      .. math::
  
          y_k = 2 \sum_{n=0}^{N-1} x_n \sin\left(\frac{\pi(k+1)(2n+1)}{2N}\right)
  
      if ``norm='ortho'``, ``y[k]`` is multiplied by a scaling factor ``f``
  
      .. math::
  
          f = \begin{cases}
          \sqrt{\frac{1}{4N}} & \text{if }k = 0, \\
          \sqrt{\frac{1}{2N}} & \text{otherwise} \end{cases}
  
      **Type III**
  
      There are several definitions of the DST-III, we use the following (for
      ``norm="backward"``). DST-III assumes the input is odd around :math:`n=-1` and
      even around :math:`n=N-1`
  
      .. math::
  
          y_k = (-1)^k x_{N-1} + 2 \sum_{n=0}^{N-2} x_n \sin\left(
          \frac{\pi(2k+1)(n+1)}{2N}\right)
  
      The (unnormalized) DST-III is the inverse of the (unnormalized) DST-II, up
      to a factor :math:`2N`. The orthonormalized DST-III is exactly the inverse of the
      orthonormalized DST-II.
  
      **Type IV**
  
      There are several definitions of the DST-IV, we use the following (for
      ``norm="backward"``). DST-IV assumes the input is odd around :math:`n=-0.5` and
      even around :math:`n=N-0.5`
  
      .. math::
  
          y_k = 2 \sum_{n=0}^{N-1} x_n \sin\left(\frac{\pi(2k+1)(2n+1)}{4N}\right)
  
      The (unnormalized) DST-IV is its own inverse, up to a factor :math:`2N`. The
      orthonormalized DST-IV is exactly its own inverse.
  
      References
      ----------
      .. [1] Wikipedia, "Discrete sine transform",
             https://en.wikipedia.org/wiki/Discrete_sine_transform
  
      """
      return (Dispatchable(x, np.ndarray),)
  
file_path: catboost/contrib/python/scipy/py3/scipy/fft/_realtransforms.py
imports: from ._basic import _dispatch
  from scipy._lib.uarray import Dispatchable
  import numpy
label_desc: Use image node_id 7 for calling a global function with example usage: dst(x, type, n, axis, norm, overwrite_x, workers) and returns: Dispatchable