code: def _write_keys_tensor(resolved_type, record, vector):
      """Placeholder Docstring"""
      if resolved_type == "Int32":
          record.features["values"].int32_tensor.keys.extend(vector)
      elif resolved_type == "Float64":
          record.features["values"].float64_tensor.keys.extend(vector)
      elif resolved_type == "Float32":
          record.features["values"].float32_tensor.keys.extend(vector)
  
imports: from __future__ import absolute_import
  import io
  import logging
  import struct
  import sys
  import numpy
  from sagemaker.amazon.record_pb2 import Record
  from sagemaker.deprecations import deprecated_class
  from sagemaker.deserializers import SimpleBaseDeserializer
  from sagemaker.serializers import SimpleBaseSerializer
  from sagemaker.utils import DeferredError
label_desc: Use image node_id 3 for calling a global function with example usage: _write_keys_tensor(resolved_type, record, vector) without return types