class_docstr: Metrics factory. Returns:
      - metric value
      - should it be maximized or minimized
      - Reduce function
  
  Maximized or minimized is needed for early stopping (saving best checkpoint)
  Reduce function to generate a single metric value, usually "mean" or "none"
class_docstr_tok: ["Metrics","factory",".","Returns",":","-","metric","value","-","should","it","be","maximized","or","minimized","-","Reduce","function","Maximized","or","minimized","is","needed","for","early","stopping","(","saving","best","checkpoint",")","Reduce","function","to","generate","a","single","metric","value",",","usually","``","mean","''","or","``","none","''"]
docstr: Access to Metrics.
  
  Args:
      name: metrics name
  Returns:
      A class to build the Metrics
docstr_tok: ["Access","to","Metrics",".","Args",":","name",":","metrics","name","Returns",":","A","class","to","build","the","Metrics"]
code: def get(cls, name: str) -> Any:
      """Access to Metrics.
  
      Args:
          name: metrics name
      Returns:
          A class to build the Metrics
      """
      return cls._metrics.get(name, cls._metrics["LogLoss"])
  
file_path: h2o-llmstudio/llm_studio/src/metrics/text_causal_classification_modeling_metrics.py
imports: import logging
  from typing import Any, Dict, List, Tuple, Union
  import numpy
  import pandas
  from numpy.typing import NDArray
  from scipy.special import softmax
  from sklearn.metrics import log_loss, roc_auc_score
label_desc: Use image node_id 2 for calling the Metrics obj's underlying member method code with example usage: obj.get(cls, name) and returns: cls