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.
class_docstr_tok: ["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","."]
docstr: Get coordinates placeholder, location name is formed
  using ``LOCATION_FORMAT``.
  Used in the queue-based tensorflow APIs.
  
  :param name: input name string
  :return: coordinates placeholder
docstr_tok: ["Get","coordinates","placeholder",",","location","name","is","formed","using","``","LOCATION_FORMAT","``",".","Used","in","the","queue-based","tensorflow","APIs",".",":","param","name",":","input","name","string",":","return",":","coordinates","placeholder"]
code: def coordinates_placeholder(self, name):
      """
      Get coordinates placeholder, location name is formed
      using ``LOCATION_FORMAT``.
      Used in the queue-based tensorflow APIs.
  
      :param name: input name string
      :return: coordinates placeholder
      """
      try:
          return self._placeholders_dict[LOCATION_FORMAT.format(name)]
      except TypeError:
          tf.logging.fatal("call placeholders_dict to initialise first")
          raise
  
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 9 for calling the ImageWindow obj's underlying member method code with example usage: obj.coordinates_placeholder(name) and returns: self