class_docstr: Hierarchical text classification model.
class_docstr_tok: ["Hierarchical","text","classification","model","."]
docstr: Input shape: [batch_size, max_len]
  New Input shape: [batch_size, max_doc_len, max_sen_len]
docstr_tok: ["Input","shape",":","[","batch_size",",","max_len","]","New","Input","shape",":","[","batch_size",",","max_doc_len",",","max_sen_len","]"]
code: def pad_to_hier_input(
      inputs, max_doc_len, max_sen_len, padding_token=0
  ):
      """
      Input shape: [batch_size, max_len]
      New Input shape: [batch_size, max_doc_len, max_sen_len]
      """
      new_len = max_sen_len * max_doc_len
      new_input = cut_or_padding(
          inputs, new_len, padding_token=padding_token
      )
      new_input = tf.reshape(new_input, [-1, max_doc_len, max_sen_len])
      return new_input
  
file_path: delta/delta/models/text_hierarchical_model.py
imports: import pickle
  from absl import logging
  import delta.compat
  import delta.utils
  import delta.layers
  from delta.layers.utils import cut_or_padding
  from delta.layers.utils import compute_sen_lens
  from delta.layers.utils import compute_doc_lens
  from delta.layers.utils import split_one_doc_to_true_len_sens
  from delta.models.text_cls_model import TextClassModel
  from delta.utils.register import registers
label_desc: Use image node_id 2 for calling the HierarchicalModel obj's underlying member method code with example usage: obj.pad_to_hier_input(inputs, max_doc_len, max_sen_len, padding_token) and returns: new_input