class_docstr: MobileNet model from the
  `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
  <https://arxiv.org/abs/1704.04861>`_ paper.
  
  Parameters
  ----------
  multiplier : float, default 1.0
      The width multiplier for controling the model size. Only multipliers that are no
      less than 0.25 are supported. The actual number of channels is equal to the original
      channel size multiplied by this multiplier.
  classes : int, default 1000
      Number of classes for the output layer.
class_docstr_tok: ["MobileNet","model","from","the","`","``","MobileNets",":","Efficient","Convolutional","Neural","Networks","for","Mobile","Vision","Applications","''","<","https",":","\/\/arxiv.org\/abs\/1704.04861",">","`","_","paper",".","Parameters","--","--","--","--","--","multiplier",":","float",",","default","1.0","The","width","multiplier","for","controling","the","model","size",".","Only","multipliers","that","are","no","less","than","0.25","are","supported",".","The","actual","number","of","channels","is","equal","to","the","original","channel","size","multiplied","by","this","multiplier",".","classes",":","int",",","default","1000","Number","of","classes","for","the","output","layer","."]
code: def forward(self, x):
      x = self.features(x)
      x = self.output(x)
      return x
  
imports: import os
  from ...None import nn
  from ....device import cpu
  from ...block import HybridBlock
  from ....None import base, np
  from ....util import use_np, wrap_ctx_to_device_func
label_desc: Use image node_id 2 for calling the MobileNet obj's underlying member method code with example usage: obj.forward(x) and returns: x