class_docstr: PartitionedFunction will translate call to cached Function call
class_docstr_tok: ["PartitionedFunction","will","translate","call","to","cached","Function","call"]
docstr: _partitions_read
code: def _partitions_read(self, indices_start, indices_stop, indices):
      """_partitions_read"""
      items = []
      # TODO: change to tf.while_loop
      for index in indices:
          slice_start = (
              indices_start[index] - self._partitions_start[index]
          )
          slice_size = indices_stop[index] - indices_start[index]
          slice_start = [slice_start] + self._slice_suffix_start
          slice_size = [slice_size] + self._slice_suffix_size
          item = tf.slice(
              self._partitions[index], slice_start, slice_size
          )
          items.append(item)
      return tf.concat(items, axis=0)
  
imports: import tensorflow
label_desc: Use image node_id 4 for calling the _IOTensorIterablePartitionedFunction obj's underlying member method code with example usage: obj._partitions_read(indices_start, indices_stop, indices) and returns: tf