def batch_tensor_dicts(tensor_dicts: List[Dict[str, torch.Tensor]],
                       remove_trailing_dimension: bool = False) -> Dict[str, torch.Tensor]:
    """
    Takes a list of tensor dictionaries, where each dictionary is assumed to have matching keys,
    and returns a single dictionary with all tensors with the same key batched together.

    Parameters
    ----------
    tensor_dicts : ``List[Dict[str, torch.Tensor]]``
        The list of tensor dictionaries to batch.
    remove_trailing_dimension : ``bool``
        If ``True``, we will check for a trailing dimension of size 1 on the tensors that are being
        batched, and remove it if we find it.
    """
    key_to_tensors: Dict[str, List[torch.Tensor]] = defaultdict(list)
    for tensor_dict in tensor_dicts:
        for key, tensor in tensor_dict.items():
            key_to_tensors[key].append(tensor)
    batched_tensors = {}
    for key, tensor_list in key_to_tensors.items():
        batched_tensor = torch.stack(tensor_list)
        if remove_trailing_dimension and all(tensor.size(-1) == 1 for tensor in tensor_list):
            batched_tensor = batched_tensor.squeeze(-1)
        batched_tensors[key] = batched_tensor
    return batched_tensors
Takes a list of tensor dictionaries, where each dictionary is assumed to have matching keys,
    and returns a single dictionary with all tensors with the same key batched together.

    Parameters
    ----------
    tensor_dicts : ``List[Dict[str, torch.Tensor]]``
        The list of tensor dictionaries to batch.
    remove_trailing_dimension : ``bool``
        If ``True``, we will check for a trailing dimension of size 1 on the tensors that are being
        batched, and remove it if we find it.