class_docstr: SVDF cell implementation.
  
  All variables are created and reused.
docstr: Adds image summaries for the given filter (an image per rank).
  
  Arguments:
    filters: A Tensor containing the filters. Expected shape: [rank *
      num_units, filter_dim]. Thus, the tensor groups the rank filters for
      each unit, and the function will split them to generate an image per
      rank. Each image will have the shape: [filter_dim, num_units]
    name: The string name to use as parth of the image log path.
docstr_tok: ["Adds","image","summaries","for","the","given","filter","(","an","image","per","rank",")",".","Arguments",":","filters",":","A","Tensor","containing","the","filters",".","Expected","shape",":","[","rank","*","num_units",",","filter_dim","]",".","Thus",",","the","tensor","groups","the","rank","filters","for","each","unit",",","and","the","function","will","split","them","to","generate","an","image","per","rank",".","Each","image","will","have","the","shape",":","[","filter_dim",",","num_units","]","name",":","The","string","name","to","use","as","parth","of","the","image","log","path","."]
code: def _add_filter_image_summary(self, filters, name):
      """Adds image summaries for the given filter (an image per rank).
  
      Arguments:
        filters: A Tensor containing the filters. Expected shape: [rank *
          num_units, filter_dim]. Thus, the tensor groups the rank filters for
          each unit, and the function will split them to generate an image per
          rank. Each image will have the shape: [filter_dim, num_units]
        name: The string name to use as parth of the image log path.
      """
      for r in range(self._rank):
          rank_filters = tf.strided_slice(
              filters, [r, 0], filters.get_shape(), [self._rank, 1]
          )
          rank_filters = tf.expand_dims(rank_filters, 0)
          rank_filters = tf.expand_dims(rank_filters, 3)
          tf.compat.v1.summary.image(
              "%s/rank_%d" % (name, (r + 1)),
              rank_filters,
              max_outputs=1,
          )
  
imports: import tensorflow.compat.v2
label_desc: Use image node_id 5 for calling the SvdfCell obj's underlying member method code with example usage: obj._add_filter_image_summary(filters, name) without return types