class_docstr: A layer that starts the propagation of masks in a model.
  
  This layer assumes that the node features given as input have been extended with a
  binary mask that indicates which nodes are valid in each graph.
  The layer is useful when using a `data.BatchLoader` with `mask=True` or in general
  when zero-padding graphs so that all batches have the same size. The binary mask
  indicates with a 1 those nodes that should be taken into account by the model.
  
  The layer will remove the rightmost feature from the nodes and start a mask
  propagation to all subsequent layers:
  
  ```python
  print(x.shape)  # shape (batch, n_nodes, n_node_features + 1)
  mask = x[..., -1:]  # shape (batch, n_nodes, 1)
  x_new = x[..., :-1] # shape (batch, n_nodes, n_node_features)
  ```
code: def call(self, inputs, **kwargs):
      # Remove mask from features
      if isinstance(inputs, list):
          inputs[0] = inputs[0][..., :-1]
      else:
          inputs = inputs[..., :-1]
  
      return inputs
  
imports: import numpy
  import tensorflow
  from tensorflow.keras import activations
  from tensorflow.keras import backend
  from tensorflow.keras import constraints, initializers, regularizers
  from tensorflow.keras.layers import Layer
  from tensorflow.python.framework import smart_cond
  from spektral.layers import ops
label_desc: Use image node_id 2 for calling the GraphMasking obj's underlying member method code with example usage: obj.call(inputs) and returns: inputs