code: def einsum_op_cuda(q, k, v):
      stats = torch.cuda.memory_stats(q.device)
      mem_active = stats["active_bytes.all.current"]
      mem_reserved = stats["reserved_bytes.all.current"]
      mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
      mem_free_torch = mem_reserved - mem_active
      mem_free_total = mem_free_cuda + mem_free_torch
      # Divide factor of safety as there's copying and fragmentation
      return einsum_op_tensor_mem(
          q, k, v, mem_free_total / 3.3 / (1 << 20)
      )
  
imports: from __future__ import annotations
  import math
  import psutil
  import platform
  import torch
  from torch import einsum
  from ldm.util import default
  from einops import rearrange
  from modules import shared, errors, devices, sub_quadratic_attention
  from modules.hypernetworks import hypernetwork
  import ldm.modules.attention
  import ldm.modules.diffusionmodules.model
  import sgm.modules.attention
  import sgm.modules.diffusionmodules.model
label_desc: Use image node_id 11 for calling a global function with example usage: einsum_op_cuda(q, k, v) and returns: einsum_op_tensor_mem