code: def execute(cls, ctx, op: "DataFrameMemoryUsage"):
      in_data = ctx[op.inputs[0].key]
      # choose correct dataframe library
      xdf = cudf if op.gpu else pd
  
      if op.stage == OperandStage.reduce:
          result = reduce(operator.add, (ctx[c.key] for c in op.inputs))
          if op.range_index_size is not None:
              if hasattr(in_data, "ndim"):
                  # dataframe input: prepend index size column
                  prepend_series = xdf.Series(
                      [op.range_index_size],
                      index=["Index"],
                      dtype=result.dtype,
                  )
                  result = xdf.concat([prepend_series, result])
              else:
                  # series input: add index size to the output
                  result += op.range_index_size
          ctx[op.outputs[0].key] = result
      elif isinstance(in_data, xdf.Index):
          ctx[op.outputs[0].key] = in_data.memory_usage(deep=op.deep)
      else:
          ctx[op.outputs[0].key] = in_data.memory_usage(
              index=op.index, deep=op.deep
          )
  
imports: import operator
  from functools import reduce
  import numpy
  import pandas
  from ...None import opcodes
  from ...config import options
  from ...core.operand import OperandStage
  from ...serialization.serializables import BoolField, Int64Field
  from ...utils import ceildiv, lazy_import
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..core import IndexValue
  from ..utils import parse_index
label_desc: Use image node_id 14 for calling the DataFrameMemoryUsage obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types