class_docstr: Diffs for a metric.
class_docstr_tok: ["Diffs","for","a","metric","."]
docstr: Add human-readable string representations to the field.
docstr_tok: ["Add","human-readable","string","representations","to","the","field","."]
code: def __post_init__(self):
      """Add human-readable string representations to the field."""
  
      if "memory" in self.name:
          self.base_value_str = format_memory(self.base_value)
          self.experimental_value_str = format_memory(
              self.experimental_value
          )
          self.diff_str = format_memory(self.diff)
      elif "time" in self.name:
          self.base_value_str = format_time(self.base_value)
          self.experimental_value_str = format_time(
              self.experimental_value
          )
          self.diff_str = format_time(self.diff)
      else:
          self.base_value_str = str(self.base_value)
          self.experimental_value_str = str(self.experimental_value)
          self.diff_str = str(self.diff)
  
imports: import csv
  import logging
  import os
  from dataclasses import dataclass
  from statistics import mean
  from typing import Dict, List, Optional, Set, Union
  import ludwig.modules.metric_modules
  from ludwig.benchmarking.utils import format_memory, format_time
  from ludwig.globals import MODEL_HYPERPARAMETERS_FILE_NAME
  from ludwig.modules.metric_registry import get_metric_classes, metric_feature_type_registry
  from ludwig.types import ModelConfigDict
  from ludwig.utils.data_utils import load_json
label_desc: Use image node_id 1 for calling the MetricDiff obj's underlying member method code with example usage: obj.__post_init__() without return types