def sanity_check(self):
        "Check the underlying data in the training set can be properly loaded."
        final_message = "You can deactivate this warning by passing `no_check=True`."
        if not hasattr(self.train_ds, 'items') or len(self.train_ds.items) == 0 or not hasattr(self.train_dl, 'batch_sampler'): return
        if len(self.train_dl) == 0:
            warn(f"""Your training dataloader is empty, you have only {len(self.train_dl.dataset)} items in your training set.
                 Your batch size is {self.train_dl.batch_size}, you should lower it.""")
            print(final_message)
            return
        idx = next(iter(self.train_dl.batch_sampler))
        samples,fails = [],[]
        for i in idx:
            try:    samples.append(self.train_dl.dataset[i])
            except: fails.append(i)
        if len(fails) > 0:
            warn_msg = "There seems to be something wrong with your dataset, for example, in the first batch can't access"
            if len(fails) == len(idx):
                warn_msg += f" any element of self.train_ds.\nTried: {show_some(idx)}"
            else:
                warn_msg += f" these elements in self.train_ds: {show_some(fails)}"
            warn(warn_msg)
            print(final_message)
            return
        try: batch = self.collate_fn(samples)
        except:
            message = "It's not possible to collate samples of your dataset together in a batch."
            try:
                shapes = [[o[i].data.shape for o in samples] for i in range(2)]
                message += f'\nShapes of the inputs/targets:\n{shapes}'
            except: pass
            warn(message)
            print(final_message)
Check the underlying data in the training set can be properly loaded.