class_docstr: Base Problem for gene expression datasets.
class_docstr_tok: ["Base","Problem","for","gene","expression","datasets","."]
code: def preprocess_example(self, example, mode, unused_hparams):
      del mode
  
      # Reshape targets to contain num_output_predictions per output timestep
      example["targets"] = tf.reshape(
          example["targets"], [-1, 1, self.num_output_predictions]
      )
      # Slice off EOS - not needed, and messes up the GeneExpressionConv model
      # which expects the input length to be a multiple of the target length.
      example["inputs"] = example["inputs"][:-1]
  
      return example
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import multiprocessing
  import os
  import h5py
  import numpy
  from six.moves import range
  from tensor2tensor.data_generators import dna_encoder
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import metrics
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 10 for calling the GeneExpressionProblem obj's underlying member method code with example usage: obj.preprocess_example(example, mode, unused_hparams) and returns: example