code: def capture_optimization_metadata(model: Model, framework) -> dict:
      """Placeholder docstring"""
      logging.info("Capturing optimization metadata...")
      # get size of the model
      model_size = sys.getsizeof(model)
      # TODO: We may need to use framework specific way to get the model size
      # example torchsummary for pytorch etc.
      # need more dive deep into this
      optimizer_metadata = defaultdict(str)
      optimizer_metadata["ModelSizeInMemory"] = model_size
  
      if framework == "pytorch":
          # TODO: get input shape if pytorch
          # self.optimizer_metadata['input-shape'] = self.schema_builder.input_schema.shape
          True  # pylint: disable=W0104
  
      logging.info("Optimization metadata captured successfully")
      return optimizer_metadata
  
imports: from __future__ import absolute_import
  import secrets
  import hmac
  import hashlib
  import os
  import logging
  import sys
  import platform
  from collections import defaultdict
  from pathlib import Path
  import tempfile
  import yaml
  from sagemaker.model import Model
  from sagemaker.session import Session
  from sagemaker.serve.builder.schema_builder import SchemaBuilder
  from sagemaker.s3_utils import parse_s3_url
  from sagemaker.utils import create_tar_file
label_desc: Use image node_id 6 for calling a global function with example usage: capture_optimization_metadata(model, framework) and returns: optimizer_metadata