To support GPU training in an object-oriented framework, a `Trainer` class must be configured to automatically manage device placement for both the data and the model. This is typically implemented by storing the target GPU devices during initialization, overriding a `prepare_batch` method to transfer incoming data minibatches to the target GPU, and overriding a `prepare_model` method to move the neural network's parameters to the exact same GPU before training begins.

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In an object-oriented deep learning architecture, the `prepare_batch` method is registered within a `Trainer` class to handle incoming minibatches before they are processed by the model. Although its default implementation may simply return the batch unchanged, it serves as an essential hook. This method can be customized by subclasses to perform preprocessing tasks or to dynamically move data to appropriate hardware accelerators before the batch is passed into the model's `training_step` or `validation_step`.

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