code: def masked_log_softmax(
      data, mask, axis=-1, temperature=1.0, normalize=True
  ):
      r"""Computes the masked log softmax of the input.
      This is equivalent to computing masked softmax followed by log.
  
      Parameters
      ----------
      data : NDArray
          The input array.
      mask : NDArray
          Mask to apply.
      axis : int, optional, default='-1'
          The axis along which to compute softmax.
      temperature : double or None, optional, default=None
          Temperature parameter in softmax
      normalize : boolean or None, optional, default=1
          Whether to normalize input data x: x = x - max(x)
  
      Returns
      -------
      out : NDArray or list of NDArrays
          The output of this function.
  
      Examples
      --------
      >>> data = np.arange(5)
      >>> mask = np.array([1, 0, 1, 0, 1])
      >>> npx.masked_log_softmax(data, mask)
      array([-4.1429286 ,        -inf, -2.1429286 ,        -inf, -0.14292854])
      >>> data = np.arange(10).reshape((2, 5))
      >>> npx.masked_log_softmax(data, mask, axis=0)
      array([[-5.0067153 ,        -inf, -5.0067153 ,        -inf, -5.0067153 ],
             [-0.00671535,        -inf, -0.00671535,        -inf, -0.00671535]])
      """
      return _mx_nd_npx.masked_log_softmax(
          data,
          mask,
          axis=axis,
          temperature=temperature,
          normalize=normalize,
      )
  
imports: from ..ndarray import numpy_extension
  from ..util import set_module
label_desc: Use image node_id 4 for calling a global function with example usage: masked_log_softmax(data, mask, axis, temperature, normalize) and returns: _mx_nd_npx