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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 get_monitor_forward_and_backward(summary_writer, bins):
 """ Get the method for monitoring the forward values of the network """

    def monitor_forward_and_backward(module, input, output):
        """
        Iterate over the module parameters and monitor their forward values.
        Then iterate over all of the monitored_vars, monitor their forward values
        and set their grad_hooks
        """
        # Parameters
        if module.is_monitoring:
            param_names = [ name for name, _ in module.named_parameters()]
            for name, param in zip(param_names, module.parameters()):
                if module.track_grad and param.requires_grad:
                    hook = grad_hook(module, name, summary_writer, bins)
                    module.param_hooks[name] = param.register_hook(hook)
                if module.track_data:
                    summary_writer.add_histogram('{}/data'.format(name.replace('.','/')),
                                                 param.detach().cpu().numpy(),
                                                 module.global_step,
                                                 bins=bins)
                if name in module.last_state_dict:
                    if module.track_update:
                        update = param - module.last_state_dict[name]
                        summary_writer.add_histogram('{}/update-val'.format(name.replace('.','/')),
                                                     update.detach().cpu().numpy(),
                                                     module.global_step-1,
                                                     bins=bins)
                    if module.track_update and module.track_update_ratio:
                        update_ratio = update / (module.last_state_dict[name]+1e-15)

                        summary_writer.add_histogram('{}/update-ratio'.format(name.replace('.','/')),
                                                     update_ratio.detach().cpu().numpy(),
                                                     module.global_step-1,
                                                     bins=bins)
                if module.track_update:
                    module.last_state_dict[name] = param.clone()

            # Intermediate Vars
            for prefix, mod in module.named_modules():
                for tensor_name, entry in mod.monitored_vars.items():
                    name = '{}/{}'.format(prefix, tensor_name) if prefix else tensor_name
                    tensor = entry['tensor']
                    if entry['track_grad'] and tensor.requires_grad:
                        hook = grad_hook(module, name, summary_writer, bins)
                        module.var_hooks[name] = tensor.register_hook(hook)
                    if entry['track_data']:
                        summary_writer.add_histogram('{}/data'.format(name.replace('.','/')),
                                                     tensor.detach().cpu().numpy(),
                                                     module.global_step,
                                                     bins=bins)
            # Update step
        module.global_step += 1
    return monitor_forward_and_backward