code: def instantiate_input(self):
      b, hs = self.rand_shape([self.b, self.hs])
  
      self.input = self.rand(
          [b, 4 * hs],
          device=self.device,
          dtype=self.dtype,
          requires_grad=self.requires_grad,
      )
      self.hx = self.rand(
          [b, 4 * hs],
          device=self.device,
          dtype=self.dtype,
          requires_grad=self.requires_grad,
      )
      self.cx = self.rand(
          [b, hs],
          device=self.device,
          dtype=self.dtype,
          requires_grad=self.requires_grad,
      )
      self.b_ih = self.rand(
          [b, 4 * hs],
          device=self.device,
          dtype=self.dtype,
          requires_grad=self.requires_grad,
      )
      self.b_hh = self.rand(
          [b, 4 * hs],
          device=self.device,
          dtype=self.dtype,
          requires_grad=self.requires_grad,
      )
      self.inputs = [
          self.input,
          self.hx,
          self.cx,
          self.b_ih,
          self.b_hh,
      ]
  
file_path: pytorch/benchmarks/tensorexpr/rnn_eltwise.py
imports: import torch
  from .None import benchmark
label_desc: Use image node_id 2 for calling the DynamicLSTM obj's underlying member method code with example usage: obj.instantiate_input() without return types