parameters: (node: torch.fx.Node, _: nn.Module)
docstring: Mapping from Tensor.size() to acc_ops.size. We map size() to acc_ops.size directly
      and map size(dim) to acc_ops.size + acc_ops.getitem.
docstring_summary: Mapping from Tensor.size() to acc_ops.size. We map size() to acc_ops.size directly
      and map size(dim) to acc_ops.size + acc_ops.getitem.
docstring_tokens: Mapping from Tensor . size () to acc_ops . size . We map size () to acc_ops . size directly and map size ( dim ) to acc_ops . size + acc_ops . getitem .
function: def tensor_size_mapper(node: torch.fx.Node, _: nn.Module) -> torch.fx.Node:
      """
      Mapping from Tensor.size() to acc_ops.size. We map size() to acc_ops.size directly
      and map size(dim) to acc_ops.size + acc_ops.getitem.
      """
  
      with node.graph.inserting_before(node):
          size_node = node.graph.call_function(
              size, kwargs={"input": node.kwargs["input"]}
          )
  
          if "dim" not in node.kwargs:
              size_node.meta = node.meta.copy()
              return size_node
  
          size_node.meta["type"] = torch.Size
          getitem_node = node.graph.call_function(
              getitem, kwargs={"input": size_node, "idx": node.kwargs["dim"]}
          )
          getitem_node.meta = node.meta.copy()
          return getitem_node
function_tokens: def tensor_size_mapper ( node : torch . fx . Node , _ : nn . Module ) -> torch . fx . Node : with node . graph . inserting_before ( node ) : size_node = node . graph . call_function ( size , kwargs = { "input" : node . kwargs [ "input" ] } ) if "dim" not in node . kwargs : size_node . meta = node . meta . copy ( ) return size_node size_node . meta [ "type" ] = torch . Size getitem_node = node . graph . call_function ( getitem , kwargs = { "input" : size_node , "idx" : node . kwargs [ "dim" ] } ) getitem_node . meta = node . meta . copy ( ) return getitem_node