code: def mean(
      a,
      weights=None,
      axis=None,
      dtype=None,
      keepdims: bool = False,
      ddof: int = 0,
  ):
      """
      Compute the weighted mean along the specified axis.
  
      :param a: Array containing numbers whose mean is desired. If `a` is not an array, a conversion is attempted.
      :param weights: Array containing weights for the elements of `a`. If `weights` is not an
          array, a conversion is attempted.
      :param axis: Axis or axes along which the means are computed. The default is to
          compute the mean of the flattened array. Type is None or int or tuple of ints, optional.
      :param dtype: data type to use in computing the mean.
      :param bool keepdims: If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one.
      :param int ddof: delta degrees of freedom
      :return: np.ndarray
      """
      if weights is None:
          return np.mean(a, axis=axis, dtype=dtype, keepdims=keepdims)
      else:
          w = np.array(weights)
  
          return np.sum(
              w * np.array(a), axis=axis, dtype=dtype, keepdims=keepdims
          ) / (
              np.sum(w, axis=axis, dtype=dtype, keepdims=keepdims)
              - ddof
          )
  
file_path: popmon/popmon/stats/numpy.py
imports: import numpy
  import pandas
label_desc: Use image node_id 1 for calling a global function with example usage: mean(a, weights, axis, dtype, keepdims, ddof) and returns: np, unknown