class_docstr: Template for any configuration file
class_docstr_tok: ["Template","for","any","configuration","file"]
docstr: Returns a set of possible values for the field provided, and the current value.
  
  Args:
      field: the field
      value: the preliminary value of the field.
      type_annotation: Type Annotation of the field.
      mode: current mode, one of {"train", "test", "predict"}.
      dataset_fn: A function returning a tuple (dataset, value). Will be called
          if the possible values depend on the dataset.
  
  Returns:
      Possible values for the field, the current value.
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code: def _get_possible_values(
      self,
      field: str,
      value: Any,
      type_annotation: type,
      mode: str,
      dataset_fn=None,
  ) -> Optional[Tuple[Optional[possible_values.Value], Any]]:
      """
      Returns a set of possible values for the field provided, and the current value.
  
      Args:
          field: the field
          value: the preliminary value of the field.
          type_annotation: Type Annotation of the field.
          mode: current mode, one of {"train", "test", "predict"}.
          dataset_fn: A function returning a tuple (dataset, value). Will be called
              if the possible values depend on the dataset.
  
      Returns:
          Possible values for the field, the current value.
      """
  
      poss_values = self._possible_values.get(field, None)
  
      if isinstance(poss_values, possible_values.DatasetValue):
          if dataset_fn is None:
              raise ValueError(
                  f"{poss_values} needs a dataset to compute possible values!\n"
                  "`dataset_fn` must be provided."
              )
  
          dataset, value = dataset_fn(field, value)
          poss_values, value = poss_values.get_value(
              dataset=dataset,
              value=value,
              type_annotation=type_annotation,
              mode=mode,
          )
      elif isinstance(poss_values, Sequence):
          if all(isinstance(x, (float, int)) for x in poss_values):
              poss_values = possible_values.Number(
                  min=poss_values[0],
                  max=poss_values[1],
                  step=poss_values[2],
              )
          elif all(isinstance(x, str) for x in poss_values):
              poss_values = possible_values.String(tuple(poss_values))
          else:
              raise ValueError(
                  f"Could not interpret {poss_values} as any possible value class."
              )
  
      return poss_values, value
  
imports: import dataclasses
  import logging
  from dataclasses import dataclass, fields
  from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
  from llm_studio.src import possible_values
  from llm_studio.src.nesting import Dependency, Nesting
  from llm_studio.src.order import Order
  from llm_studio.src.tooltips import tooltips
label_desc: Use image node_id 2 for calling the DefaultConfig obj's underlying member method code with example usage: obj._get_possible_values(field, value, type_annotation, mode, dataset_fn) and returns: poss_values,  value