code: def text_cnn_base():
      """Set of hyperparameters."""
      hparams = common_hparams.basic_params1()
      hparams.batch_size = 4096
      hparams.max_length = 256
      hparams.clip_grad_norm = 0.0  # i.e. no gradient clipping
      hparams.optimizer_adam_epsilon = 1e-9
      hparams.learning_rate_schedule = "legacy"
      hparams.learning_rate_decay_scheme = "noam"
      hparams.learning_rate = 0.1
      hparams.learning_rate_warmup_steps = 4000
      hparams.initializer_gain = 1.0
      hparams.num_hidden_layers = 6
      hparams.initializer = "uniform_unit_scaling"
      hparams.weight_decay = 0.0
      hparams.optimizer_adam_beta1 = 0.9
      hparams.optimizer_adam_beta2 = 0.98
      hparams.num_sampled_classes = 0
      hparams.label_smoothing = 0.1
      hparams.shared_embedding_and_softmax_weights = True
      hparams.symbol_modality_num_shards = 16
  
      # Add new ones like this.
      hparams.add_hparam("filter_sizes", [2, 3, 4, 5])
      hparams.add_hparam("num_filters", 128)
      hparams.add_hparam("output_dropout", 0.4)
      return hparams
  
file_path: tensor2tensor/tensor2tensor/models/text_cnn.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: text_cnn_base() and returns: hparams