code: def prepare_inputs(bsr, *dense_tensors):
      # Introduce fake batch dimension if not present for convenience.
      crow_indices = bsr.crow_indices().unsqueeze(0)
      col_indices = bsr.col_indices().unsqueeze(0)
      values = make_triton_contiguous(bsr.values().unsqueeze(0))
      tensors = [
          make_triton_contiguous(t.unsqueeze(0)) for t in dense_tensors
      ]
  
      # Compute broadcasted batch dimension
      batch_dims_broadcasted = torch.broadcast_shapes(
          values.shape[:-3], *(t.shape[:-2] for t in tensors)
      )
  
      # Broadcast batch dimensions and squash.
      # The result can be either a view or a copy.
      def batch_broadcast_and_squash(t, batch_dims, invariant_dims):
          return t.broadcast_to(batch_dims + invariant_dims).flatten(
              0, len(batch_dims) - 1
          )
  
      crow_indices = batch_broadcast_and_squash(
          crow_indices, batch_dims_broadcasted, (-1,)
      )
  
      col_indices = batch_broadcast_and_squash(
          col_indices, batch_dims_broadcasted, (-1,)
      )
      values = batch_broadcast_and_squash(
          values, batch_dims_broadcasted, values.shape[-3:]
      )
      tensors = [
          batch_broadcast_and_squash(
              t, batch_dims_broadcasted, t.shape[-2:]
          )
          for t in tensors
      ]
  
      return crow_indices, col_indices, values, *tensors
  
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 14 for calling a global function with example usage: prepare_inputs(bsr) and returns: crow_indices,  col_indices,  values,  unknown, t