docstr: Repeat states to match the the shape induced by the number of sample
  paths.
  
  Parameters
  ----------
  begin_states
      List of initial states for the RNN layers (batch_size, num_cells)
  
  Returns
  -------
      List of initial states
docstr_tok: ["Repeat","states","to","match","the","the","shape","induced","by","the","number","of","sample","paths",".","Parameters","--","--","--","--","--","begin_states","List","of","initial","states","for","the","RNN","layers","(","batch_size",",","num_cells",")","Returns","--","--","--","-","List","of","initial","states"]
code: def make_states(self, begin_states: List[Tensor]) -> List[Tensor]:
      """
      Repeat states to match the the shape induced by the number of sample
      paths.
  
      Parameters
      ----------
      begin_states
          List of initial states for the RNN layers (batch_size, num_cells)
  
      Returns
      -------
          List of initial states
      """
  
      def repeat(tensor):
          return tensor.repeat(
              repeats=self.num_parallel_samples, axis=0
          )
  
      return [repeat(s) for s in begin_states]
  
imports: from typing import List, Optional, Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
  from gluonts.mx.distribution import Distribution, DistributionOutput
  from gluonts.mx.util import assert_shape, weighted_average
label_desc: Use image node_id 9 for calling the DeepVARNetwork obj's underlying member method code with example usage: obj.make_states(begin_states) and returns: unknown, tensor