class_docstr: A general class for convolutional layers.
  
  You can extend this class to create custom implementations of GNN layers
  that use standard matrix multiplication instead of the gather-scatter
  approach of MessagePassing.
  
  This is useful if you want to create layers that support dense inputs,
  batch and mixed modes, or other non-standard processing. No checks are done
  on the inputs, to allow for maximum flexibility.
  
  Any extension of this class must implement the `call(self, inputs)` and
  `config(self)` methods.
  
  **Arguments**:
  
  - ``**kwargs`: additional keyword arguments specific to Keras' Layers, like
  regularizers, initializers, constraints, etc.
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code: def build(self, input_shape):
      self.built = True
  
file_path: spektral/spektral/layers/convolutional/conv.py
imports: import warnings
  from functools import wraps
  import tensorflow
  from tensorflow.keras.layers import Layer
  from spektral.utils.keras import deserialize_kwarg, is_keras_kwarg, is_layer_kwarg, serialize_kwarg
label_desc: Use image node_id 2 for calling the Conv obj's underlying member method code with example usage: obj.build(input_shape) without return types