class_docstr: A Batch of training/evaluation data.
class_docstr_tok: ["A","Batch","of","training\/evaluation","data","."]
docstr: Zip placeholders and batch data into a feed dict.
  
  Args:
    placeholders: placeholder dictionary. Must have three keys "pianorolls",
        "masks" and "lengths".
  
  Returns:
    A feed dict mapping the given placeholders to the data in this batch.
docstr_tok: ["Zip","placeholders","and","batch","data","into","a","feed","dict",".","Args",":","placeholders",":","placeholder","dictionary",".","Must","have","three","keys","``","pianorolls","''",",","``","masks","''","and","``","lengths","''",".","Returns",":","A","feed","dict","mapping","the","given","placeholders","to","the","data","in","this","batch","."]
code: def get_feed_dict(self, placeholders):
      """Zip placeholders and batch data into a feed dict.
  
      Args:
        placeholders: placeholder dictionary. Must have three keys "pianorolls",
            "masks" and "lengths".
  
      Returns:
        A feed dict mapping the given placeholders to the data in this batch.
      """
      assert set(placeholders.keys()) == self.keys
      return dict(
          (placeholders[key], self.features[key]) for key in self.keys
      )
  
imports: import os
  from magenta.models.coconet import lib_mask
  from magenta.models.coconet import lib_pianoroll
  from magenta.models.coconet import lib_util
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the Batch obj's underlying member method code with example usage: obj.get_feed_dict(placeholders) and returns: dict