code: def broadcast_batch_dims_bsr(f_name, bsr, *tensors):
      batch_shape = broadcast_batch_dims(f_name, bsr, *tensors)
  
      crow_indices = bsr.crow_indices().broadcast_to(
          batch_shape + (-1,)
      )
      col_indices = bsr.col_indices().broadcast_to(batch_shape + (-1,))
      values = bsr.values().broadcast_to(
          batch_shape + bsr.values().shape[-3:]
      )
      size = batch_shape + bsr.shape[-2:]
      return torch.sparse_compressed_tensor(
          crow_indices,
          col_indices,
          values,
          size=size,
          layout=bsr.layout,
      )
  
imports: import math
  import os
  import torch
  import weakref
  from functools import lru_cache
  from torch.utils._triton import has_triton
  from ._triton_ops_meta import get_meta
  from typing import Optional, Tuple
label_desc: Use image node_id 15 for calling a global function with example usage: broadcast_batch_dims_bsr(f_name, bsr) and returns: torch