How can I implement a custom module in Sonnet

1. Define a Subclass of `snt.Module`: Create a new Python class that inherits from `snt.Module`. This will be your custom module.

python
import sonnet as snt

class MyCustomModule(snt.Module):
    def __init__(self, output_size, name=None):
        super().__init__(name=name)
        self.output_size = output_size
        
    def __call__(self, inputs):
        # Implement your custom module logic here
        output = # some computation on inputs
        return output

2. Initialize the Module: Create an instance of your custom module class, just like any other Sonnet module.

python
my_module = MyCustomModule(output_size=10)

3. Use the Module in Your Model: You can now use your custom module as a building block in your neural network model.

python
class MyModel(snt.Module):
    def __init__(self, name=None):
        super().__init__(name=name)
        self.custom_module = MyCustomModule(output_size=10)
        
    def __call__(self, inputs):
        x = self.custom_module(inputs)
        # Add more layers or computations here
        return x

model = MyModel()
output = model(input_data)

The key points are:

- Define a subclass of `snt.Module` to encapsulate your custom module logic.
- Implement the `__call__` method to define the computation performed by your module.
- Initialize your custom module and use it as a component in your overall model.

This allows you to create reusable, composable modules that can be easily integrated into your Sonnet-based neural network architectures. The flexibility of Sonnet's module system enables you to build complex models by combining custom and pre-defined modules.

Remember to follow the Sonnet conventions, such as managing variables within the module, using the `name` parameter, and adhering to Sonnet's best practices for module design and usage.