code: def generate_inputs(self):
      h_shape = (self.n_layers * 2, self.batches[0], self.hidden_size)
      dtype = self.in_dtypes[0]
  
      h = array_utils.uniform(h_shape, dtype)
      in_size = self.input_size
      out_size = self.hidden_size
      xs = [
          array_utils.uniform((self.batches[b], in_size), dtype)
          for b in range(len(self.batches))
      ]
  
      def w_in(i, j):
          if i == 0 and j < 3:
              return in_size
          elif i > 0 and j < 3:
              return out_size * 2
          else:
              return out_size
  
      inputs = []
      inputs.append(h)
      for i in range(len(self.batches)):
          inputs.append(xs[i])
  
      for n in range(self.n_layers):
          for direction in (0, 1):
              for i in range(6):
                  inputs.append(
                      array_utils.uniform((out_size, w_in(n, i)), dtype)
                  )
              for i in range(6):
                  inputs.append(array_utils.uniform((out_size,), dtype))
      return tuple(inputs)
  
imports: import chainer
  import chainerx
  from chainerx_tests import array_utils
  from chainerx_tests import dtype_utils
  from chainerx_tests import op_utils
label_desc: Use image node_id 2 for calling the TestNStepBiGRU obj's underlying member method code with example usage: obj.generate_inputs() and returns: tuple, in_size, unknown, out_size