class_docstr: Concatenates weight matrices for cuDNN's RNN.
  
  This function concatenates weight matrices for RNNs into one large array.
  Its format is defined in cuDNN's API.
code: def forward_gpu(self, inputs):
      handle = cudnn.get_handle()
      ws_size = self.n_layers * self.rnn_direction * self.n_W
      ws = inputs[:ws_size]
      bs = inputs[ws_size:]
      out_size = ws[0].shape[0]
      in_size = ws[0].shape[1]
      dtype = ws[0].dtype
      cudnn_data_type = cudnn.get_data_type(dtype)
  
      # TODO(unno): Make a wrapper method to avoid access _desc directly
      rnn_desc = cudnn.create_rnn_descriptor(
          out_size,
          self.n_layers,
          self.states._desc,
          libcudnn.CUDNN_LINEAR_INPUT,
          self.rnn_dir,
          self.rnn_mode,
          cudnn_data_type,
      )
      self.rnn_desc = rnn_desc
  
      dummy_x = cuda.cupy.empty((1, in_size, 1), dtype=dtype)
      x_desc = cudnn.create_tensor_nd_descriptor(dummy_x)
  
      weights_size = libcudnn.getRNNParamsSize(
          handle, rnn_desc.value, x_desc.value, cudnn_data_type
      )
      byte_size = dtype.itemsize
      w = cuda.cupy.empty(
          (weights_size // byte_size, 1, 1), dtype=dtype
      )
      w_desc = cudnn.create_filter_descriptor(w)
  
      for layer in six.moves.range(self.n_layers):
          for di in six.moves.range(self.rnn_direction):
              mat_index = layer * self.rnn_direction + di
              # di = 0: forward, 1: backward
              for lin_layer_id in six.moves.range(self.n_W):
                  mat = cudnn.get_rnn_lin_layer_matrix_params(
                      handle,
                      rnn_desc,
                      mat_index,
                      x_desc,
                      w_desc,
                      w,
                      lin_layer_id,
                  )
                  W_index = mat_index * self.n_W + lin_layer_id
                  m = mat.reshape(mat.size)
                  m[...] = ws[W_index].ravel()
                  bias = cudnn.get_rnn_lin_layer_bias_params(
                      handle,
                      rnn_desc,
                      mat_index,
                      x_desc,
                      w_desc,
                      w,
                      lin_layer_id,
                  )
                  b = bias.reshape(bias.size)
                  b[...] = bs[W_index]
      self.w_desc = w_desc
      self.x_desc = x_desc
      return (w,)
  
imports: import itertools
  import numpy
  import six
  import chainer
  import chainerx
  from chainer import backend
  from chainer import variable
  from chainer.backends import cuda
  from chainer import configuration
  from chainer import function
  from chainer.functions.activation import relu
  from chainer.functions.activation import tanh
  from chainer.functions.array import concat
  from chainer.functions.array import split_axis
  from chainer.functions.array import stack
  from chainer.functions.connection import linear
  from chainer.functions.noise import dropout
  from chainer.utils import argument
  from chainer.utils import type_check
label_desc: Use image node_id 3 for calling the CudnnRNNWeightConcat obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: w