class_docstr: Feature extractor.
  
  Parameters
  ----------
  network : str or HybridBlock or Symbol
      Logic chain: load from gluoncv.model_zoo if network is string.
      Convert to Symbol if network is HybridBlock
  outputs : str or list of str
      The name of layers to be extracted as features
  inputs : list of str or list of Symbol
      The inputs of network.
  pretrained : bool
      Use pretrained parameters as in gluon.model_zoo
  ctx : Context
      The context, e.g. mxnet.cpu(), mxnet.gpu(0).
code: def __init__(
      self,
      network,
      outputs,
      inputs=("data",),
      pretrained=False,
      ctx=mx.cpu(),
      **kwargs,
  ):
      inputs, outputs, params = _parse_network(
          network, outputs, inputs, pretrained, ctx, **kwargs
      )
      super(FeatureExtractor, self).__init__(
          outputs, inputs, params=params
      )
  
file_path: gluon-cv/gluoncv/nn/feature.py
imports: from __future__ import absolute_import
  import mxnet
  from mxnet.base import string_types
  from mxnet.gluon import HybridBlock, SymbolBlock
  from mxnet.symbol import Symbol
  from mxnet.symbol.contrib import SyncBatchNorm
label_desc: Use image node_id 1 to create a new FeatureExtractor object from inherited base classes: SymbolBlock with example: obj = FeatureExtractor(network, outputs, inputs, pretrained, ctx)