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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def train_batch(batch_list, context, network, gluon_trainer):
 """ Training with multiple GPUs Parameters batch_list: List list of dataset context: List a list of all GPUs to be used for training network: ResNet gluon_trainer: rain module of gluon """

    # Split and load data into multiple GPUs
    data = batch_list[0]
    data = gluon.utils.split_and_load(data, context)

    # Split and load label into multiple GPUs
    label = batch_list[1]
    label = gluon.utils.split_and_load(label, context)

    # Run the forward and backward pass
    forward_backward(network, data, label)

    # Update the parameters
    this_batch_size = batch_list[0].shape[0]
    gluon_trainer.step(this_batch_size)