code: def conv_linear_map(inputs, nin, nout, bias_start, prefix):
      """Convolutional liner map.
  
      Maps 3D tensor by last dimension.
  
      Args:
        inputs: Inputs that should be shuffled
        nin: Input feature map count
        nout: Output feature map count
        bias_start: Bias start value
        prefix: Name prefix
  
      Returns:
        tf.Tensor: Inputs with applied convolution
      """
  
      with tf.variable_scope(prefix):
          inp_shape = tf.shape(inputs)
  
          initializer = tf.variance_scaling_initializer(
              scale=1.0, mode="fan_avg", distribution="uniform"
          )
          kernel = tf.get_variable(
              "CvK", [nin, nout], initializer=initializer
          )
          bias_term = tf.get_variable(
              "CvB", [nout], initializer=tf.constant_initializer(0.0)
          )
  
          mul_shape = [inp_shape[0] * inp_shape[1], nin]
          res = tf.matmul(tf.reshape(inputs, mul_shape), kernel)
          res = tf.reshape(res, [inp_shape[0], inp_shape[1], nout])
          return res + bias_start + bias_term
  
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 5 for calling a global function with example usage: conv_linear_map(inputs, nin, nout, bias_start, prefix) and returns: unknown