class_docstr: An instance of a metric definition object.
  
  An instance is different from the metric definition object in that it correlates to columns in a data set and can be
  in different states. e.g. a measure instance can be aggregated, or a time dimension can be at a different
  granularity.
class_docstr_tok: ["An","instance","of","a","metric","definition","object",".","An","instance","is","different","from","the","metric","definition","object","in","that","it","correlates","to","columns","in","a","data","set","and","can","be","in","different","states",".","e.g",".","a","measure","instance","can","be","aggregated",",","or","a","time","dimension","can","be","at","a","different","granularity","."]
docstr: Helper for getting the associated column until support for multiple associated columns is added.
code: def associated_column(self) -> ColumnAssociation:
      """Helper for getting the associated column until support for multiple associated columns is added."""
      assert len(self.associated_columns) == 1
      return self.associated_columns[0]
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Generic, List, Tuple, TypeVar
  from dbt_semantic_interfaces.dataclass_serialization import SerializableDataclass
  from dbt_semantic_interfaces.references import MetricModelReference, SemanticModelElementReference
  from metricflow.aggregation_properties import AggregationState
  from metricflow.specs.column_assoc import ColumnAssociation
  from metricflow.specs.specs import DimensionSpec, EntitySpec, InstanceSpec, InstanceSpecSet, MeasureSpec, MetadataSpec, MetricSpec, TimeDimensionSpec
label_desc: Use image node_id 1 for calling the MdoInstance obj's underlying member method code with example usage: obj.associated_column() and returns: self