class_docstr: This leaves the input unchanged during masking, and is used for things like scoring labels.
  
  Sometimes there are inputs to the model that we do not want to explain, but rather we want to
  consider them fixed. The primary example of this is when we explain the loss of the model using
  the labels. These "true" labels are inputs to the function we are explaining, but we don't want
  to attribute credit to them, instead we want to consider them fixed and assign all the credit to
  the model's input features. This is where the Fixed masker can help, since we can apply it to the
  label inputs.
docstr: The shape of the masks we expect.
          
docstr_tok: ["The","shape","of","the","masks","we","expect","."]
code: def mask_shapes(self, x):
      """The shape of the masks we expect."""
      return [(0,)]
  
imports: import numpy
  from ._masker import Masker
label_desc: Use image node_id 3 for calling the Fixed obj's underlying member method code with example usage: obj.mask_shapes(x) and returns: list