code: def get_matcher_miner_func(
      miner_type: str,
      pos_margin: float,
      neg_margin: float,
      distance_type: str,
  ):
      """
      Return a pytorch metric learning's miner functions based on their names.
      The miners are used to mine the positive and negative examples.
  
      Parameters
      ----------
      miner_type
          The miner function type.
      pos_margin
          The positive margin used by the miner function.
      neg_margin
          The negative margin used by the miner function.
      distance_type
          The distance function type.
  
      Returns
      -------
      A miner function to mine positive and negative samples.
      """
      if miner_type.lower() == PAIR_MARGIN_MINER:
          return miners.PairMarginMiner(
              pos_margin=pos_margin,
              neg_margin=neg_margin,
              distance=get_metric_learning_distance_func(distance_type),
          )
      else:
          raise ValueError(
              f"Unknown metric learning miner: {miner_type}"
          )
  
imports: import functools
  import logging
  import re
  import warnings
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  import torchmetrics
  from omegaconf import DictConfig, OmegaConf
  from packaging import version
  from pytorch_metric_learning import distances, losses, miners
  from torch import nn, optim
  from torch.nn import functional
  from torchmetrics.detection.mean_ap import MeanAveragePrecision
  from transformers import Adafactor
  from transformers.models.mask2former.modeling_mask2former import Mask2FormerConfig, Mask2FormerLoss
  from transformers.trainer_pt_utils import get_parameter_names
  from ..constants import ACC, ACCURACY, AVERAGE_PRECISION, BER, BINARY, BIT_FIT, COLUMN_FEATURES, CONTRASTIVE_LOSS, COSINE_EMBEDDING_LOSS, COSINE_SIMILARITY, CROSS_ENTROPY, DETECTION_METRICS, DIRECT_LOSS, EM, F1, F1_MACRO, F1_MICRO, F1_WEIGHTED, FEATURES, FEW_SHOT_CLASSIFICATION, FM, HIT_RATE, IA3, IA3_BIAS, IA3_LORA, IA3_LORA_BIAS, IA3_LORA_NORM, IA3_NORM, IOU, LOG_LOSS, LORA, LORA_BIAS, LORA_NORM, MAE, MULTI_NEGATIVES_SOFTMAX_LOSS, MULTICLASS, NER, NER_TOKEN_F1, NORM_FIT, OBJECT_DETECTION, OPEN_VOCABULARY_OBJECT_DETECTION, OVERALL_ACCURACY, PAIR_MARGIN_MINER, PEARSONR, PEFT_STRATEGIES, QUADRATIC_KAPPA, R2, RECALL, REGRESSION, RMSE, ROC_AUC, ROOT_MEAN_SQUARED_ERROR, SEMANTIC_SEGMENTATION, SM, SPEARMANR
  from .losses import BBCEWithLogitLoss, FocalLoss, MultiNegativesSoftmaxLoss, SoftTargetCrossEntropy, StructureLoss
  from .lr_scheduler import get_cosine_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup
  from .semantic_seg_metrics import COD_METRICS_NAMES, Balanced_Error_Rate, Binary_IoU, Multiclass_IoU
label_desc: Use image node_id 17 for calling a global function with example usage: get_matcher_miner_func(miner_type, pos_margin, neg_margin, distance_type) and returns: miners