class_docstr: Data Preprocessing.
  
  Base class for applying common real-time data preprocessing.
  
  This class is meant to be used as an argument of `input_data`. When training
  a model, the defined pre-processing methods will be applied at both
  training and testing time. Note that DataAugmentation is similar to
  DataPreprocessing, but only applies at training time.
  
  Arguments:
      None.
  
  Parameters:
      methods: `list of function`. Augmentation methods to apply.
      args: A `list` of arguments to use for these methods.
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docstr: Compute std of a dataset. A limit can be specified for faster
  computation, considering only 'limit' first elements. 
docstr_tok: ["Compute","std","of","a","dataset",".","A","limit","can","be","specified","for","faster","computation",",","considering","only","'limit","'","first","elements","."]
code: def _compute_global_std(self, dataset, session, limit=None):
      """Compute std of a dataset. A limit can be specified for faster
      computation, considering only 'limit' first elements."""
      _dataset = dataset
      std = 0.0
      if isinstance(limit, int):
          _dataset = _dataset[:limit]
      if isinstance(_dataset, np.ndarray):
          std = np.std(_dataset)
      else:
          for i in range(len(dataset)):
              std += np.std(dataset[i]) / len(dataset)
      self.global_std.assign(std, session)
      return std
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  import pickle
  import tensorflow.compat.v1
label_desc: Use image node_id 17 for calling the DataPreprocessing obj's underlying member method code with example usage: obj._compute_global_std(dataset, session, limit) and returns: std