class_docstr: Estimator implementation for Image Classification.
  
  Parameters
  ----------
  config : dict
      Config in nested dict.
  logger : logging.Logger
      Optional logger for this estimator, can be `None` when default setting is used.
  reporter : callable
      The reporter for metric checkpointing.
  net : mx.gluon.Block
      The custom network. If defined, the model name in config will be ignored so your
      custom network will be used for training rather than pulling it from model zoo.
class_docstr_tok: ["Estimator","implementation","for","Image","Classification",".","Parameters","--","--","--","--","--","config",":","dict","Config","in","nested","dict",".","logger",":","logging.Logger","Optional","logger","for","this","estimator",",","can","be","`","None","`","when","default","setting","is","used",".","reporter",":","callable","The","reporter","for","metric","checkpointing",".","net",":","mx.gluon.Block","The","custom","network",".","If","defined",",","the","model","name","in","config","will","be","ignored","so","your","custom","network","will","be","used","for","training","rather","than","pulling","it","from","model","zoo","."]
code: def _predict_feature(self, x, ctx_id=0):
      x = self._predict_preprocess(x)
      if isinstance(x, pd.DataFrame):
          assert (
              "image" in x.columns
          ), "Expect column `image` for input images"
          df = self._predict_feature(tuple(x["image"]))
          df = df.set_index(x.index)
          df["image"] = x["image"]
          return df
      elif isinstance(x, (list, tuple)):
          assert isinstance(
              x[0], str
          ), "expect image paths in list/tuple input"
          bs = self._cfg.valid.batch_size
          feat_net = self._get_feature_net()
          feat_net.hybridize()
          results = []
          loader = mx.gluon.data.DataLoader(
              ImageListDataset(x, self._predict_preprocess),
              batch_size=bs,
              last_batch="keep",
          )
          for batch in loader:
              batch = mx.gluon.utils.split_and_load(
                  batch, ctx_list=self.ctx, even_split=False
              )
              feats = [feat_net(input) for input in batch]
              for p in feats:
                  for ii in range(p.shape[0]):
                      feat = p[ii].asnumpy().flatten()
                      results.append({"image_feature": feat})
          df = pd.DataFrame(results)
          df["image"] = x
          return df
      elif not isinstance(x, mx.nd.NDArray):
          raise ValueError("Input is not supported: {}".format(type(x)))
      assert (
          len(x.shape) == 4 and x.shape[1] == 3
      ), "Expect input to be (n, 3, h, w), given {}".format(x.shape)
      x = x.as_in_context(self.ctx[ctx_id])
      feat_net = self._get_feature_net()
      results = []
      for ii in range(x.shape[0]):
          feat = feat_net(x)[ii].asnumpy().flatten()
          results.append({"image_feature": feat})
      df = pd.DataFrame(results)
      return df
  
imports: import time
  import os
  import math
  import copy
  from PIL import Image
  import pandas
  import numpy
  import mxnet
  from mxnet import gluon, nd
  from mxnet.optimizer import Optimizer
  from mxnet import autograd
  from mxnet.gluon.data.vision import transforms
  from ....data.transforms.presets.imagenet import transform_eval
  from ....model_zoo import get_model
  from ....utils import LRSequential, LRScheduler
  from ....None import nn
  from ....None import loss
  from ..base_estimator import BaseEstimator, set_default
  from ..constants import BINARY, MULTICLASS, REGRESSION
  from .utils import get_data_loader, get_data_rec, smooth, mixup_transform
  from .default import ImageClassificationCfg
  from ...data.dataset import ImageClassificationDataset
  from ..conf import _BEST_CHECKPOINT_FILE
  from ..utils import EarlyStopperOnPlateau
label_desc: Use image node_id 12 for calling the ImageClassificationEstimator obj's underlying member method code with example usage: obj._predict_feature(x, ctx_id) and returns: df, df, df