original_docstring: Forward prop through a stack of conformer layers.
  
      Args:
        inputs: input features of shape [b, t, d], where d == params.input_dims.
        paddings: associated paddings of shape [b, t], with 1's where the input is
          not valid
  
      Returns:
        output of final encoder layer of shape [b, t, model_dims]
  
      Note future versions can stack / repeat the layers and scan over layers
      to improve compile time efficiency larger models.
      
docstring: Forward prop through a stack of conformer layers.
docstring_tokens: Forward prop through a stack of conformer layers .
code: def fprop(self, inputs: JTensor, paddings: JTensor) -> JTensor:
      """Forward prop through a stack of conformer layers.
  
      Args:
        inputs: input features of shape [b, t, d], where d == params.input_dims.
        paddings: associated paddings of shape [b, t], with 1's where the input is
          not valid
  
      Returns:
        output of final encoder layer of shape [b, t, model_dims]
  
      Note future versions can stack / repeat the layers and scan over layers
      to improve compile time efficiency larger models.
      """
      p = self.params
  
      x = inputs
      for i in range(0, p.num_layers):
        x = self.layers[i].fprop(x, paddings)
  
      return x
code_tokens: def fprop ( self , inputs : JTensor , paddings : JTensor ) -> JTensor : p = self . params x = inputs for i in range ( 0 , p . num_layers ) : x = self . layers [ i ] . fprop ( x , paddings ) return x
short_docstring: Forward prop through a stack of conformer layers.
short_docstring_tokens: Forward prop through a stack of conformer layers .
comment: """Forward prop through a stack of conformer layers.
  
      Args:
        inputs: input features of shape [b, t, d], where d == params.input_dims.
        paddings: associated paddings of shape [b, t], with 1's where the input is
          not valid
  
      Returns:
        output of final encoder layer of shape [b, t, model_dims]
  
      Note future versions can stack / repeat the layers and scan over layers
      to improve compile time efficiency larger models.
      """