code: def memory_workload(self):
      if self.mode == "fwd":
          sol_count = {"i": 1, "o": 1, "k": 1}
          algorithmic_count = {"i": 1, "o": 1, "k": 1}
      else:
          sol_count = {"i": 1 + 1, "o": 1 + 1, "k": 1 + 1}
          algorithmic_count = {
              "i": 1 + (1 + 1),
              "o": 1 + (1 + 1),
              "k": 1 + (1 + 1),
          }
  
      buffer_size = {
          "i": self.N * self.iC * self.H * self.W,
          "o": self.N * self.oC * self.H * self.W,
          "k": self.oC
          * (self.iC / self.groups)
          * self.kernel_size
          * self.kernel_size,
      }
      sol_size = 0
      algorithmic_size = 0
      for key in sol_count:
          sol_size += buffer_size[key] * sol_count[key]
          algorithmic_size += buffer_size[key] * algorithmic_count[key]
      return {"sol": sol_size, "algorithmic": algorithmic_size}
  
file_path: pytorch/benchmarks/tensorexpr/conv.py
imports: from .None import benchmark
label_desc: Use image node_id 4 for calling the ConvImplBench obj's underlying member method code with example usage: obj.memory_workload() and returns: dict