class_docstr: Class for loading data.
docstr: Given a list of indices, return the potentially augmented batch.
docstr_tok: ["Given","a","list","of","indices",",","return","the","potentially","augmented","batch","."]
code: def _get_batch_from_indices(self, indices):
      """Given a list of indices, return the potentially augmented batch."""
      x_batch = []
      seq_len = []
      for idx in range(len(indices)):
          i = indices[idx]
          data = self.random_scale(self.strokes[i])
          data_copy = np.copy(data)
          if self.augment_stroke_prob > 0:
              data_copy = augment_strokes(
                  data_copy, self.augment_stroke_prob
              )
          x_batch.append(data_copy)
          length = len(data_copy)
          seq_len.append(length)
      seq_len = np.array(seq_len, dtype=int)
      # We return three things: stroke-3 format, stroke-5 format, list of seq_len.
      return (
          x_batch,
          self.pad_batch(x_batch, self.max_seq_length),
          seq_len,
      )
  
imports: import random
  import numpy
label_desc: Use image node_id 7 for calling the DataLoader obj's underlying member method code with example usage: obj._get_batch_from_indices(indices) and returns: x_batch,  self,  seq_len