code: def bincount(x, weights=None, minlength=None):
      """
      bincount(x, /, weights=None, minlength=0)
  
      Count number of occurrences of each value in array of non-negative ints.
  
      The number of bins (of size 1) is one larger than the largest value in
      `x`. If `minlength` is specified, there will be at least this number
      of bins in the output array (though it will be longer if necessary,
      depending on the contents of `x`).
      Each bin gives the number of occurrences of its index value in `x`.
      If `weights` is specified the input array is weighted by it, i.e. if a
      value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead
      of ``out[n] += 1``.
  
      Parameters
      ----------
      x : array_like, 1 dimension, nonnegative ints
          Input array.
      weights : array_like, optional
          Weights, array of the same shape as `x`.
      minlength : int, optional
          A minimum number of bins for the output array.
  
          .. versionadded:: 1.6.0
  
      Returns
      -------
      out : ndarray of ints
          The result of binning the input array.
          The length of `out` is equal to ``np.amax(x)+1``.
  
      Raises
      ------
      ValueError
          If the input is not 1-dimensional, or contains elements with negative
          values, or if `minlength` is negative.
      TypeError
          If the type of the input is float or complex.
  
      See Also
      --------
      histogram, digitize, unique
  
      Examples
      --------
      >>> np.bincount(np.arange(5))
      array([1, 1, 1, 1, 1])
      >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
      array([1, 3, 1, 1, 0, 0, 0, 1])
  
      >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
      >>> np.bincount(x).size == np.amax(x)+1
      True
  
      The input array needs to be of integer dtype, otherwise a
      TypeError is raised:
  
      >>> np.bincount(np.arange(5, dtype=float))
      Traceback (most recent call last):
        ...
      TypeError: Cannot cast array data from dtype('float64') to dtype('int64')
      according to the rule 'safe'
  
      A possible use of ``bincount`` is to perform sums over
      variable-size chunks of an array, using the ``weights`` keyword.
  
      >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
      >>> x = np.array([0, 1, 1, 2, 2, 2])
      >>> np.bincount(x,  weights=w)
      array([ 0.3,  0.7,  1.1])
  
      """
      return (x, weights)
  
imports: import functools
  from .None import overrides
  from .None import _multiarray_umath
  from ._multiarray_umath import *
  from ._multiarray_umath import _flagdict, from_dlpack, _place, _reconstruct, _vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version, _get_madvise_hugepage, _set_madvise_hugepage, _get_promotion_state, _set_promotion_state
label_desc: Use image node_id 11 for calling a global function with example usage: bincount(x, weights, minlength) and returns: x,  weights