class_docstr: Train the SVM for the low-shot image classification tasks. Currently,
  datasets like VOC07 and Places205 are supported.
  
  The trained inherits from the SVMTrainer class and takes care of
  training SVM, evaluating, and aggregate the metrics.
class_docstr_tok: ["Train","the","SVM","for","the","low-shot","image","classification","tasks",".","Currently",",","datasets","like","VOC07","and","Places205","are","supported",".","The","trained","inherits","from","the","SVMTrainer","class","and","takes","care","of","training","SVM",",","evaluating",",","and","aggregate","the","metrics","."]
code: def __init__(self, config, layer, output_dir):
      super().__init__(config, layer, output_dir)
      self._dataset_name = config["low_shot"]["dataset_name"]
      self.cls_list = []
      self.num_classes = None
  
file_path: vissl/vissl/utils/svm_utils/svm_low_shot_trainer.py
imports: import logging
  import pickle
  import numpy
  from iopath.common.file_io import g_pathmgr
  from sklearn.svm import LinearSVC
  from vissl.utils.io import load_file, save_file
  from vissl.utils.svm_utils.evaluate import get_precision_recall
  from vissl.utils.svm_utils.svm_trainer import SVMTrainer
label_desc: Use image node_id 1 to create a new SVMLowShotTrainer object from inherited base classes: SVMTrainer with example: obj = SVMLowShotTrainer(config, layer, output_dir)