code: def generate_inputs(self):
      h_shape = (self.n_layers * 2, self.batches[0], self.hidden_size)
      dtype = self.in_dtypes[0]
      low = -1.0
      high = 1.0
      if dtype == "float16":
          low = -0.5
          high = 0.5
  
      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, low=low, high=high
          )
          for b in range(len(self.batches))
      ]
  
      def w_in(i, j):
          if i == 0 and j < 1:
              return in_size
          elif i > 0 and j < 1:
              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(2):
                  inputs.append(
                      array_utils.uniform(
                          (out_size, w_in(n, i)),
                          dtype,
                          low=low,
                          high=high,
                      )
                  )
              for i in range(2):
                  inputs.append(
                      array_utils.uniform(
                          (out_size,), dtype, low=low, high=high
                      )
                  )
      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 TestNStepBiRNN obj's underlying member method code with example usage: obj.generate_inputs() and returns: tuple, in_size, unknown, out_size