class_docstr: Describes a set of data that a source node in the dataflow plan contains.
class_docstr_tok: ["Describes","a","set","of","data","that","a","source","node","in","the","dataflow","plan","contains","."]
docstr: Extracts all metric time TimeDimensionInstances from the InstanceSet associated with this DataSet.
docstr_tok: ["Extracts","all","metric","time","TimeDimensionInstances","from","the","InstanceSet","associated","with","this","DataSet","."]
code: def metric_time_dimension_instances(
      self,
  ) -> Sequence[TimeDimensionInstance]:
      """Extracts all metric time TimeDimensionInstances from the InstanceSet associated with this DataSet."""
      return tuple(
          time_dimension_instance
          for time_dimension_instance in self.instance_set.time_dimension_instances
          if time_dimension_instance.spec.element_name
          == DataSet.metric_time_dimension_name()
      )
  
file_path: metricflow/metricflow/dataset/dataset.py
imports: from __future__ import annotations
  import logging
  from typing import Optional, Sequence
  from dbt_semantic_interfaces.references import TimeDimensionReference
  from dbt_semantic_interfaces.type_enums.date_part import DatePart
  from dbt_semantic_interfaces.type_enums.time_granularity import TimeGranularity
  from dbt_semantic_interfaces.validations.unique_valid_name import MetricFlowReservedKeywords
  from metricflow.instances import InstanceSet, TimeDimensionInstance
  from metricflow.specs.specs import TimeDimensionSpec
label_desc: Use image node_id 3 for calling the DataSet obj's underlying member method code with example usage: obj.metric_time_dimension_instances() and returns: tuple