class_docstr: MobileNetV2 model from the
  `"Inverted Residuals and Linear Bottlenecks:
  Mobile Networks for Classification, Detection and Segmentation"
  <https://arxiv.org/abs/1801.04381>`_ paper.
  
  Parameters
  ----------
  multiplier : float, default 1.0
      The width multiplier for controling the model size. 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: ["MobileNetV2","model","from","the","`","``","Inverted","Residuals","and","Linear","Bottlenecks",":","Mobile","Networks","for","Classification",",","Detection","and","Segmentation","''","<","https",":","\/\/arxiv.org\/abs\/1801.04381",">","`","_","paper",".","Parameters","--","--","--","--","--","multiplier",":","float",",","default","1.0","The","width","multiplier","for","controling","the","model","size",".","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 MobileNetV2 obj's underlying member method code with example usage: obj.forward(x) and returns: x