class_docstr: Switch layer of Neural Shuffle-Exchange network.
docstr: 2 input to 2 output linear map.
  
  Args:
    inputs: Input tensor
    suffix: Linear map name suffix
    bias_start: Bias start value
    in_units: Size of input tensor feature map count
    out_units: Size of output tensor feature map count
  Return:
    tf.Tensor: Convolution apply to input tensor
docstr_tok: ["2","input","to","2","output","linear","map",".","Args",":","inputs",":","Input","tensor","suffix",":","Linear","map","name","suffix","bias_start",":","Bias","start","value","in_units",":","Size","of","input","tensor","feature","map","count","out_units",":","Size","of","output","tensor","feature","map","count","Return",":","tf.Tensor",":","Convolution","apply","to","input","tensor"]
code: def linear_map(self, inputs, suffix, bias_start, in_units, out_units):
      """2 input to 2 output linear map.
  
      Args:
        inputs: Input tensor
        suffix: Linear map name suffix
        bias_start: Bias start value
        in_units: Size of input tensor feature map count
        out_units: Size of output tensor feature map count
      Return:
        tf.Tensor: Convolution apply to input tensor
      """
      in_shape = [self.batch_size, self.length // 2, in_units * 2]
      inputs = tf.reshape(inputs, in_shape)
      res = conv_linear_map(
          inputs,
          in_units * 2,
          out_units * 2,
          bias_start,
          self.prefix + "/" + suffix,
      )
      return tf.reshape(res, [self.batch_size, self.length, out_units])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling the SwitchLayer obj's underlying member method code with example usage: obj.linear_map(inputs, suffix, bias_start, in_units, out_units) and returns: tf