class_docstr: Fully connected predictor.
  Fully connected predictor is used to ignore spatial information and will
  output fixed-sized predictions.
  
  
  Parameters
  ----------
  num_output : int
      Number of fully connected outputs.
  activation : str, optional
      Optional activation after conv, e.g. 'relu'.
  use_bias : bool
      Use bias in convolution. It is not necessary if BatchNorm is followed.
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code: def __init__(
      self, num_output, activation=None, use_bias=True, **kwargs
  ):
      super(FCPredictor, self).__init__(**kwargs)
      with self.name_scope():
          self.predictor = nn.Dense(
              num_output, activation=activation, use_bias=use_bias
          )
  
file_path: gluon-cv/gluoncv/nn/predictor.py
imports: from __future__ import absolute_import
  import mxnet
  from mxnet.gluon import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new FCPredictor object from inherited base classes: HybridBlock with example: obj = FCPredictor(num_output, activation, use_bias)