code: def memory_workload(self):
      def memsize(t):
          return t.numel() * t.element_size()
  
      input_size = (
          memsize(self.att_query)
          + memsize(self.att_keys)
          + memsize(self.normalize_bias)
          + memsize(self.linear_att)
      )
      output_size = 4 * torch.Size([self.b, self.t_q, self.t_k]).numel()
      io_size = input_size + output_size
  
      # If matmul is not fused, must write and then read `sum_qk`.
      intermediate_size = (
          2
          * 4
          * torch.Size([self.b, self.t_q, self.t_k, self.n]).numel()
      )
      return {
          "sol": io_size,
          "algorithmic": io_size + intermediate_size,
      }
  
file_path: pytorch/benchmarks/tensorexpr/attention.py
imports: import torch
  from .None import benchmark
label_desc: Use image node_id 6 for calling the BahdanauAttention obj's underlying member method code with example usage: obj.memory_workload() and returns: dict, unknown