parameters: (data, mask, axis=-1, temperature=1.0, normalize=True)
return_statement: return _api_internal.masked_log_softmax(data, mask, axis, temperature, normalize)
docstring: 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]])
docstring_summary: r"""Computes the masked log softmax of the input.
      This is equivalent to computing masked softmax followed by log.
docstring_tokens: r Computes the masked log softmax of the input . This is equivalent to computing masked softmax followed by log .
function: 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]])
      """
      assert data is not None and mask is not None, "Missing input data and mask"
      return _api_internal.masked_log_softmax(data, mask, axis, temperature, normalize)
function_tokens: def masked_log_softmax ( data , mask , axis = - 1 , temperature = 1.0 , normalize = True ) : assert data is not None and mask is not None , "Missing input data and mask" return _api_internal . masked_log_softmax ( data , mask , axis , temperature , normalize )