class_docstr: Stores hyperparameters for initialization, batch norm and training.
class_docstr_tok: ["Stores","hyperparameters","for","initialization",",","batch","norm","and","training","."]
docstr: Update the default parameters through string or keyword arguments.
  
  This __init__ provides two ways to initialize default parameters, either by
  passing a string representation of a a Python dictionary containing
  hyperparameter to value mapping or by passing those hyperparameter values
  directly as keyword arguments.
  
  Args:
    *unused_args: A tuple of arguments. This first expected argument is a
        string representation of a Python dictionary containing hyperparameter
        to value mapping. For example, {"num_layers":8, "num_filters"=128}.
    **init_hparams: Keyword arguments for setting hyperparameters.
  
  Raises:
    ValueError: When incoming hparams are not in class _defaults.
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code: def __init__(self, *unused_args, **init_hparams):
      """Update the default parameters through string or keyword arguments.
  
      This __init__ provides two ways to initialize default parameters, either by
      passing a string representation of a a Python dictionary containing
      hyperparameter to value mapping or by passing those hyperparameter values
      directly as keyword arguments.
  
      Args:
        *unused_args: A tuple of arguments. This first expected argument is a
            string representation of a Python dictionary containing hyperparameter
            to value mapping. For example, {"num_layers":8, "num_filters"=128}.
        **init_hparams: Keyword arguments for setting hyperparameters.
  
      Raises:
        ValueError: When incoming hparams are not in class _defaults.
      """
      tf.logging.info("Instantiating hparams...")
      unknown_params = set(init_hparams) - set(
          Hyperparameters._defaults
      )
      if unknown_params:
          raise ValueError(
              "Unknown hyperparameters: %s" % unknown_params
          )
      self.update(Hyperparameters._defaults)
      self.update(init_hparams)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  import os
  from magenta.models.coconet import lib_util
  import numpy
  import six
  import tensorflow.compat.v1
  import yaml
label_desc: Use image node_id 1 to create a new Hyperparameters object from inherited base classes: object with example: obj = Hyperparameters()