class_docstr: When joining data sets, this class helps to figure out the necessary partition specs to join on.
docstr: Returns the specs from the instance set that correspond to partition specs.
docstr_tok: ["Returns","the","specs","from","the","instance","set","that","correspond","to","partition","specs","."]
code: def _get_partitions(
      self, spec_set: InstanceSpecSet
  ) -> PartitionSpecSet:
      """Returns the specs from the instance set that correspond to partition specs."""
      partition_dimension_specs = tuple(
          x
          for x in spec_set.dimension_specs
          if self._semantic_model_lookup.get_dimension(
              dimension_reference=x.reference
          ).is_partition
      )
      partition_time_dimension_specs = tuple(
          x
          for x in spec_set.time_dimension_specs
          if x.reference != DataSet.metric_time_dimension_reference()
          and self._semantic_model_lookup.get_time_dimension(
              time_dimension_reference=x.reference
          ).is_partition
      )
  
      return PartitionSpecSet(
          dimension_specs=partition_dimension_specs,
          time_dimension_specs=partition_time_dimension_specs,
      )
  
file_path: metricflow/metricflow/dataflow/builder/partitions.py
imports: from __future__ import annotations
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import List, Sequence, Tuple
  from metricflow.dataset.dataset import DataSet
  from metricflow.protocols.semantics import SemanticModelAccessor
  from metricflow.specs.specs import DimensionSpec, InstanceSpecSet, PartitionSpecSet, TimeDimensionSpec
label_desc: Use image node_id 2 for calling the PartitionJoinResolver obj's underlying member method code with example usage: obj._get_partitions(spec_set) and returns: PartitionSpecSet