src/transformers/pipelines/base.py/_pad
def _pad(items, key, padding_value, padding_side):
    batch_size = len(items)
    if isinstance(items[0][key], torch.Tensor):
        # Others include `attention_mask` etc...
        shape = items[0][key].shape
        dim = len(shape)
        if key in ["pixel_values", "image"]:
            # This is probable image so padding shouldn't be necessary
            # B, C, H, W
            return torch.cat([item[key] for item in items], dim=0)
        elif dim == 4 and key == "input_features":
            # this is probably a mel spectrogram batched
            return torch.cat([item[key] for item in items], dim=0)
        max_length = max(item[key].shape[1] for item in items)
        min_length = min(item[key].shape[1] for item in items)
        dtype = items[0][key].dtype

        if dim == 2:
            if max_length == min_length:
                # Bypass for `ImageGPT` which doesn't provide a padding value, yet
                # we can consistently pad since the size should be matching
                return torch.cat([item[key] for item in items], dim=0)
            tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
        elif dim == 3:
            tensor = torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype) + padding_value

        for i, item in enumerate(items):
            if dim == 2:
                if padding_side == "left":
                    tensor[i, -len(item[key][0]) :] = item[key][0].clone()
                else:
                    tensor[i, : len(item[key][0])] = item[key][0].clone()
            elif dim == 3:
                if padding_side == "left":
                    tensor[i, -len(item[key][0]) :, :] = item[key][0].clone()
                else:
                    tensor[i, : len(item[key][0]), :] = item[key][0].clone()
        return tensor
    else:
        return [item[key] for item in items]