class_docstr: `AugmentedLSTMCell` implements a AugmentedLSTM cell.
  
  # Parameters
  
  embed_dim : `int`
      The number of expected features in the input.
  lstm_dim : `int`
      Number of features in the hidden state of the LSTM.
  use_highway : `bool`, optional (default = `True`)
      If `True` we append a highway network to the outputs of the LSTM.
  use_bias : `bool`, optional (default = `True`)
      If `True` we use a bias in our LSTM calculations, otherwise we don't.
  
  # Attributes
  
  input_linearity : `nn.Module`
      Fused weight matrix which computes a linear function over the input.
  state_linearity : `nn.Module`
      Fused weight matrix which computes a linear function over the states.
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code: def reset_parameters(self):
      # Use sensible default initializations for parameters.
      block_orthogonal(
          self.input_linearity.weight.data,
          [self.lstm_dim, self.embed_dim],
      )
      block_orthogonal(
          self.state_linearity.weight.data,
          [self.lstm_dim, self.lstm_dim],
      )
  
      self.state_linearity.bias.data.fill_(0.0)
      # Initialize forget gate biases to 1.0 as per An Empirical
      # Exploration of Recurrent Network Architectures, (Jozefowicz, 2015).
      self.state_linearity.bias.data[
          self.lstm_dim : 2 * self.lstm_dim
      ].fill_(1.0)
  
imports: from typing import Optional, Tuple
  import torch
  from allennlp.common.checks import ConfigurationError
  from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence, pad_packed_sequence
  from allennlp.nn.initializers import block_orthogonal
  from allennlp.nn.util import get_dropout_mask
label_desc: Use image node_id 2 for calling the AugmentedLSTMCell obj's underlying member method code with example usage: obj.reset_parameters() without return types