code: def _np_prod(a, axis=None, dtype=None, out=None, keepdims=False):
      """
      Return the product of array elements over a given axis.
  
      Parameters
      ----------
      a : ndarray
          Input data.
      axis : None or int or tuple of ints, optional
          Axis or axes along which a product is performed.
          The default (`axis` = `None`) is perform a product over all
          the dimensions of the input array. `axis` may be negative, in
          which case it counts from the last to the first axis.
          If this is a tuple of ints, a product is performed on multiple
          axes, instead of a single axis or all the axes as before.
      dtype : data-type, optional
          The data-type of the returned array, as well as of the accumulator
          in which the elements are multiplied.  By default, if `a` is of
          integer type, `dtype` is the default platform integer. (Note: if
          the type of `a` is unsigned, then so is `dtype`.)  Otherwise,
          the dtype is the same as that of `a`.
      out : ndarray, optional
          Alternative output array in which to place the result. It must have
          the same shape as the expected output, but the type of the
          output values will be cast if necessary.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one. With this option,
          the result will broadcast correctly against the original `arr`.
  
      Returns
      -------
      product_along_axis : ndarray, see `dtype` parameter above.
          An array shaped as `a` but with the specified axis removed.
          Returns a reference to `out` if specified.
  
      See Also
      --------
      ndarray.prod : equivalent method
  
      Notes
      -----
      Arithmetic is modular when using integer types, and no error is
      raised on overflow.  That means that, on a 32-bit platform:
  
      >>> x = np.array([536870910, 536870910, 536870910, 536870910])
      >>> np.prod(x) #random
      array(8.307675e+34)
  
      Examples
      --------
      By default, calculate the product of all elements:
  
      >>> np.prod(np.array([1.,2.]))
      array(2.)
  
      Even when the input array is two-dimensional:
  
      >>> np.prod(np.array([1.,2.,3.,4.]).reshape((2,2)))
      array(24.)
  
      But we can also specify the axis over which to multiply:
  
      >>> np.prod(np.array([1.,2.,3.,4.]).reshape((2,2)), axis=1)
      array([  2.,  12.])
  
      If the type of `x` is unsigned, then the output type is
      the unsigned platform integer:
  
      >>> x = np.array([1, 2, 3], dtype=np.uint8)
      >>> np.prod(x).dtype == np.uint8
      True
  
      If `x` is of a signed integer type, then the output type
      is the default platform integer:
  
      >>> x = np.array([1, 2, 3], dtype=np.int8)
      >>> np.prod(x).dtype == np.int8
      True
      """
      pass
  
file_path: mxnet/python/mxnet/_numpy_op_doc.py
label_desc: Use image node_id 8 for calling a global function with example usage: _np_prod(a, axis, dtype, out, keepdims) without return types