class_docstr: Defines parameter ranges for tuning.
class_docstr_tok: ["Defines","parameter","ranges","for","tuning","."]
docstr: Check if name is in orig_ctr or in one of the other type containers.
docstr_tok: ["Check","if","name","is","in","orig_ctr","or","in","one","of","the","other","type","containers","."]
code: def _check_reset_and_type_change(self, name, orig_ctr):
      """Check if name is in orig_ctr or in one of the other type containers."""
      # Resetting a hyperparameter
      if name in orig_ctr:
          tf.logging.warning("Overwriting hparam %s", name)
  
      ctr_names = [
          (self._categorical_params, "categorical"),
          (self._discrete_params, "discrete"),
          (self._float_params, "float"),
          (self._int_params, "int"),
      ]
      ctrs, names = list(zip(*ctr_names))
      orig_name = names[ctrs.index(orig_ctr)]
  
      for ctr, ctr_name in ctr_names:
          if ctr is orig_ctr:
              continue
  
          # Using a different type for the same hyperparameter name
          if name in ctr:
              raise ValueError(
                  "Setting hyperparameter %s as type %s, but a "
                  "hyperparemeter of the same name was originally "
                  "registered as type %s" % (name, ctr_name, orig_name)
              )
  
file_path: tensor2tensor/tensor2tensor/layers/common_hparams.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import zip
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the RangedHParams obj's underlying member method code with example usage: obj._check_reset_and_type_change(name, orig_ctr) without return types