parameters: (self, input_feat)
return_statement: return freq_inputs
docstring: Make the frequency features.
  
      Args:
        input_feat: input Tensor, 2D, batch x num_units.
  
      Returns:
        A list of frequency features, with each element containing:
        - A 2D, batch x output_dim, Tensor representing the time-frequency feature
          for that frequency index. Here output_dim is feature_size.
      Raises:
        ValueError: if input_size cannot be inferred from static shape inference.
docstring_summary: Make the frequency features.
docstring_tokens: Make the frequency features .
function: def _make_tf_features(self, input_feat):
      """Make the frequency features.
  
      Args:
        input_feat: input Tensor, 2D, batch x num_units.
  
      Returns:
        A list of frequency features, with each element containing:
        - A 2D, batch x output_dim, Tensor representing the time-frequency feature
          for that frequency index. Here output_dim is feature_size.
      Raises:
        ValueError: if input_size cannot be inferred from static shape inference.
      """
      input_size = input_feat.get_shape().with_rank(2)[-1].value
      if input_size is None:
        raise ValueError("Cannot infer input_size from static shape inference.")
      num_feats = int((input_size - self._feature_size) / (
          self._frequency_skip)) + 1
      freq_inputs = []
      for f in range(num_feats):
        cur_input = array_ops.slice(input_feat, [0, f*self._frequency_skip],
                                    [-1, self._feature_size])
        freq_inputs.append(cur_input)
      return freq_inputs
function_tokens: def _make_tf_features ( self , input_feat ) : input_size = input_feat . get_shape ( ) . with_rank ( 2 ) [ - 1 ] . value if input_size is None : raise ValueError ( "Cannot infer input_size from static shape inference." ) num_feats = int ( ( input_size - self . _feature_size ) / ( self . _frequency_skip ) ) + 1 freq_inputs = [ ] for f in range ( num_feats ) : cur_input = array_ops . slice ( input_feat , [ 0 , f * self . _frequency_skip ] , [ - 1 , self . _feature_size ] ) freq_inputs . append ( cur_input ) return freq_inputs