class_docstr: LinearBottleneck used in MobileNetV2 model from the
  `"Inverted Residuals and Linear Bottlenecks:
  Mobile Networks for Classification, Detection and Segmentation"
  <https://arxiv.org/abs/1801.04381>`_ paper.
  
  Parameters
  ----------
  in_channels : int
      Number of input channels.
  channels : int
      Number of output channels.
  t : int
      Layer expansion ratio.
  stride : int
      stride
class_docstr_tok: ["LinearBottleneck","used","in","MobileNetV2","model","from","the","`","``","Inverted","Residuals","and","Linear","Bottlenecks",":","Mobile","Networks","for","Classification",",","Detection","and","Segmentation","''","<","https",":","\/\/arxiv.org\/abs\/1801.04381",">","`","_","paper",".","Parameters","--","--","--","--","--","in_channels",":","int","Number","of","input","channels",".","channels",":","int","Number","of","output","channels",".","t",":","int","Layer","expansion","ratio",".","stride",":","int","stride"]
code: def forward(self, x):
      out = self.out(x)
      if self.use_shortcut:
          out = np.add(out, x)
      return out
  
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 LinearBottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out