class_docstr: Each window is associated with a tuple of coordinates.
  These data properties are used to create TF
  placeholders or ``tf.data.Dataset`` when constructing a TF graph.
  Sampler read the data specifications and fill the placeholder/dataset.
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docstr: This function create a dictionary with items of
  ``{name: placeholders}``
  name should match the queue input names
  placeholders corresponds to the image window data
  for each of these items an additional ``{location_name: placeholders}``
  is created to hold the spatial location of the image window.
  Used in the queue-based tensorflow APIs.
  
  :param n_samples: specifies the number of image windows
  :return: a dictionary with window data and locations placeholders
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code: def placeholders_dict(self, n_samples=1):
      """
      This function create a dictionary with items of
      ``{name: placeholders}``
      name should match the queue input names
      placeholders corresponds to the image window data
      for each of these items an additional ``{location_name: placeholders}``
      is created to hold the spatial location of the image window.
      Used in the queue-based tensorflow APIs.
  
      :param n_samples: specifies the number of image windows
      :return: a dictionary with window data and locations placeholders
      """
  
      if self._placeholders_dict is not None:
          return self._placeholders_dict
      self._update_placeholders_dict(n_samples)
      return self._placeholders_dict
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import argparse
  import copy
  import numpy
  import tensorflow
  from tensorflow.python.data.util import nest
  from niftynet.utilities.util_common import ParserNamespace
label_desc: Use image node_id 8 for calling the ImageWindow obj's underlying member method code with example usage: obj.placeholders_dict(n_samples) and returns: self, self