docstr: Parameters
  ----------
  label
      Name of the column that contains the target variable to predict.
  hyperparameters
      This is to override some default configurations.
      example:
          hyperparameters = {
              "model.hf_text.checkpoint_name": "sentence-transformers/all-mpnet-base-v2",
              "model.hf_text.pooling_mode": "mean",
              "env.per_gpu_batch_size": 32,
              "env.eval_batch_size_ratio": 4,
          }
  presets
      Presets regarding model quality, e.g., best_quality, high_quality, and medium_quality.
  eval_metric
      Evaluation metric name.
  path
      Path to directory where models and intermediate outputs should be saved.
      If unspecified, a time-stamped folder called "AutogluonAutoMM/ag-[TIMESTAMP]"
      will be created in the working directory to store all models.
      Note: To call `fit()` twice and save all results of each fit,
      you must specify different `path` locations or don't specify `path` at all.
      Otherwise files from first `fit()` will be overwritten by second `fit()`.
  problem_type
      The problem type specified by user. Currently the SVM predictor only supports classification types
code: def __init__(
      self,
      label: Optional[str] = None,
      problem_type: Optional[str] = None,
      hyperparameters: Optional[dict] = None,
      presets: Optional[str] = None,
      eval_metric: Optional[str] = None,
      path: Optional[str] = None,
      verbosity: Optional[int] = 2,
      warn_if_exist: Optional[bool] = True,
      enable_progress_bar: Optional[bool] = None,
      **kwargs,
  ):
      """
      Parameters
      ----------
      label
          Name of the column that contains the target variable to predict.
      hyperparameters
          This is to override some default configurations.
          example:
              hyperparameters = {
                  "model.hf_text.checkpoint_name": "sentence-transformers/all-mpnet-base-v2",
                  "model.hf_text.pooling_mode": "mean",
                  "env.per_gpu_batch_size": 32,
                  "env.eval_batch_size_ratio": 4,
              }
      presets
          Presets regarding model quality, e.g., best_quality, high_quality, and medium_quality.
      eval_metric
          Evaluation metric name.
      path
          Path to directory where models and intermediate outputs should be saved.
          If unspecified, a time-stamped folder called "AutogluonAutoMM/ag-[TIMESTAMP]"
          will be created in the working directory to store all models.
          Note: To call `fit()` twice and save all results of each fit,
          you must specify different `path` locations or don't specify `path` at all.
          Otherwise files from first `fit()` will be overwritten by second `fit()`.
      problem_type
          The problem type specified by user. Currently the SVM predictor only supports classification types
      """
      super().__init__(
          label=label,
          problem_type=problem_type,
          presets=presets,
          eval_metric=eval_metric,
          hyperparameters=hyperparameters,
          path=path,
          verbosity=verbosity,
          warn_if_exist=warn_if_exist,
          enable_progress_bar=enable_progress_bar,
      )
  
      self._svm = None
  
imports: import logging
  import os
  import pickle
  from datetime import timedelta
  from typing import Dict, List, Optional, Union
  import lightning.pytorch
  import numpy
  import pandas
  import torch
  from omegaconf import DictConfig
  from sklearn.pipeline import Pipeline, make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import SVC
  from torch import nn
  from autogluon.core.metrics import Scorer
  from autogluon.core.utils.loaders import load_pd
  from ..constants import CLIP, COLUMN_FEATURES, HF_TEXT, TIMM_IMAGE, Y_PRED, Y_TRUE
  from ..data import BaseDataModule, MultiModalFeaturePreprocessor
  from ..utils import CustomUnpickler, LogFilter, apply_log_filter, compute_score, data_to_df, extract_from_output, get_available_devices, logits_to_prob, select_model, turn_on_off_feature_column_info
  from .base import BaseLearner
label_desc: Use image node_id 1 to create a new FewShotSVMLearner object from inherited base classes: BaseLearner with example: obj = FewShotSVMLearner(label, problem_type, hyperparameters, presets, eval_metric, path, verbosity, warn_if_exist, enable_progress_bar)