code: def lstm(
      x,
      h,
      c,
      w0_init=None,
      w_init=None,
      b_init=None,
      num_layers=1,
      dropout=0.0,
      bidirectional=False,
      training=True,
      rng=None,
      with_bias=True,
      fix_parameters=False,
  ):
      """LSTM (long short-term memory).
  
      Long Short-Term Memory, or LSTM, is a building block for recurrent neural networks (RNN) layers.
      LSTM unit consists of a cell and input, output, forget gates whose functions are defined as following:
  
      .. math::
          f_t&&=\\sigma(W_fx_t+U_fh_{t-1}+b_f) \\\\
          i_t&&=\\sigma(W_ix_t+U_ih_{t-1}+b_i) \\\\
          o_t&&=\\sigma(W_ox_t+U_oh_{t-1}+b_o) \\\\
          c_t&&=f_t\\odot c_{t-1}+i_t\\odot\\tanh(W_cx_t+U_ch_{t-1}+b_c) \\\\
          h_t&&=o_t\\odot\\tanh(c_t).
  
      We use the following notations to describe the inputs and outputs below.
      :math:`T`: sequcne length, :math:`B`: batch size, :math:`I`: input size, :math:`L`: number of layers, :math:`D`: number of directions, can be either 1 or 2, :math:`H`: hidden size.
  
      References:
  
          S. Hochreiter, and J. Schmidhuber. "Long Short-Term Memory."
          Neural Computation. 1997.
  
      Args:
          x (~nnabla.Variable): Input N-D array with shape :math:`(T, B, I)`.
          h (~nnabla.Variable): Input N-D array with shape :math:`(L, D, B, H)`.
          c (~nnabla.Variable): Input N-D array with shape :math:`(L, D, B, H)` .
          w0_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for weight at the first layer. Shape is :math:`(D, 4, H, I + H)`.
          w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for weights at the second layer and up. Shape is :math:`(L-1, D, 4, H, D * H + H)`.
          b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for bias. Shape is :math:`(L, D, 4, H)`.
          num_layers (int, optional): Number of layers in the network. If set to 1, only the weights for the first layer will be invoked. Default is 1.
          dropout (float, optional): Dropout ratio applied to parameters. Default is 0.0.
          bidirectional (bool, optional): If True, bidirectional computation will be performed in each layer. Default is False.
          training (bool, optional): Backpropagation will be performed only when it is true. Default is True.
          with_bias (bool, optional): Specify whether to include the bias term.
          fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
  
      Returns:
          ~nnabla.Variable: Output :math:`y` with shape :math:`(T, B, D * H)`
          ~nnabla.Variable: Output :math:`h_n` with shape :math:`(L, D, B, H)`
          ~nnabla.Variable: Output :math:`c_n` with shape :math:`(L, D, B, H)`
  
      Example:
          .. code-block:: python
  
              x = nn.Variable((seq_len, batch_size, input_size))
              h = nn.Variable((num_layers, num_directions, batch_size, hidden_size))
              c = nn.Variable((num_layers, num_directions, batch_size, hidden_size))
              y, hn, cn = PF.lstm(x, h, c)
  
      """
      if type(w0_init) == int:
          nn.logger.warning(
              "Arguments passed seem to be for previous LSTM function, which has been renamed to lstm_cell."
          )
          raise ValueError
  
      input_size = x.shape[2]
      hidden_size = h.shape[3]
      num_layers = h.shape[0]
      num_directions = 2 if bidirectional else 1
  
      w0 = get_parameter("weight_l0")
      w = get_parameter("weight")
      b = get_parameter("bias")
  
      if w0 is None:
          if w0_init is None:
              w0_ih = UniformInitializer(
                  calc_uniform_lim_glorot(input_size, hidden_size), rng
              )
              w0_ih = w0_ih(
                  (num_directions, 4, hidden_size, input_size)
              )
              w0_hh = UniformInitializer(
                  calc_uniform_lim_glorot(hidden_size, hidden_size), rng
              )
              w0_hh = w0_hh(
                  (num_directions, 4, hidden_size, hidden_size)
              )
              w0_init = np.concatenate((w0_ih, w0_hh), axis=3)
          w0_shape = (
              num_directions,
              4,
              hidden_size,
              input_size + hidden_size,
          )
          w0 = get_parameter_or_create(
              "weight_l0", w0_shape, w0_init, True, not fix_parameters
          )
  
      if num_layers > 1 and w is None:
          if w_init is None:
              w_ih = UniformInitializer(
                  calc_uniform_lim_glorot(
                      num_directions * hidden_size, hidden_size
                  ),
                  rng,
              )
              w_ih = w_ih(
                  (
                      num_layers - 1,
                      num_directions,
                      4,
                      hidden_size,
                      num_directions * hidden_size,
                  )
              )
              w_hh = UniformInitializer(
                  calc_uniform_lim_glorot(hidden_size, hidden_size), rng
              )
              w_hh = w_hh(
                  (
                      num_layers - 1,
                      num_directions,
                      4,
                      hidden_size,
                      hidden_size,
                  )
              )
              w_init = np.concatenate((w_ih, w_hh), axis=4)
          w_shape = (
              num_layers - 1,
              num_directions,
              4,
              hidden_size,
              num_directions * hidden_size + hidden_size,
          )
          w = get_parameter_or_create(
              "weight", w_shape, w_init, True, not fix_parameters
          )
  
      if with_bias and b is None:
          if b_init is None:
              b_init = ConstantInitializer()
          n_outmaps = (num_layers, num_directions, 4, hidden_size)
          b = get_parameter_or_create(
              "bias", n_outmaps, b_init, True, not fix_parameters
          )
  
      if w0.shape != (
          num_directions,
          4,
          hidden_size,
          input_size + hidden_size,
      ):
          nn.logger.warning(
              "Parameters seem to have been saved prior to bug fix. It will be converted into the correct shape, but we highly recommend training again to obtain the correct parameters, as we will cease to support these parametetrs in future. We apologize for the inconveinences."
          )
          tmp = w0.d
          w0 = nn.Variable.from_numpy_array(
              np.reshape(
                  tmp,
                  (
                      num_directions,
                      4,
                      hidden_size,
                      input_size + hidden_size,
                  ),
              ),
              need_grad=True,
          )
          nn.set_parameter("weight_l0", w0)
      if num_layers > 1 and w.shape != (
          num_layers - 1,
          num_directions,
          4,
          hidden_size,
          num_directions * hidden_size + hidden_size,
      ):
          tmp = w.d
          ww = nn.Variable.from_numpy_array(
              np.reshape(
                  tmp,
                  (
                      num_layers - 1,
                      num_directions,
                      4,
                      hidden_size,
                      num_directions * hidden_size + hidden_size,
                  ),
              ),
              need_grad=True,
          )
          nn.set_parameter("weight", w)
  
      w0 = w0.get_unlinked_variable(need_grad=not fix_parameters)
      if num_layers > 1:
          w = w.get_unlinked_variable(need_grad=not fix_parameters)
      if with_bias:
          b = b.get_unlinked_variable(need_grad=not fix_parameters)
  
      return F.lstm(
          x,
          h,
          c,
          weight_l0=w0,
          weight=w,
          bias=b,
          num_layers=num_layers,
          dropout=dropout,
          bidirectional=bidirectional,
          training=training,
      )
  
imports: from six import exec_
  import numpy
  import nnabla
  import nnabla.functions
  from nnabla.parameter import get_parameter_or_create, get_parameter
  from nnabla.initializer import calc_uniform_lim_glorot, ConstantInitializer, NormalInitializer, UniformInitializer, WeightNormalizationScaleInitializer
label_desc: Use image node_id 18 for calling a global function with example usage: lstm(x, h, c, w0_init, w_init, b_init, num_layers, dropout, bidirectional, training, rng, with_bias, fix_parameters) and returns: F