code: def setUp(self, seed=0):
      torch.manual_seed(seed)
  
      self.input1_size = random.randint(1, 1000)
      self.input2_size = random.randint(1, 100000)
      self.feature_size = random.randint(1, 256)
  
      self.input1_float = torch.randn(
          size=(self.input1_size, self.feature_size),
      ).cuda()
      self.input2_float = torch.randn(
          size=(self.input2_size, self.feature_size),
      ).cuda()
      self.index1 = torch.randint(
          low=0, high=self.input1_size, size=(self.input2_size,)
      ).cuda()
  
      self.input1_float_ = self.input1_float.clone()
      self.input2_float_ = self.input2_float.clone()
  
      self.input1_float.requires_grad_()
      self.input1_float_.requires_grad_()
      self.input2_float.requires_grad_()
      self.input2_float_.requires_grad_()
  
      self.input1_half = (
          torch.randn(
              size=(self.input1_size, self.feature_size),
          )
          .cuda()
          .half()
      )
      self.input2_half = (
          torch.randn(
              size=(self.input2_size, self.feature_size),
          )
          .cuda()
          .half()
      )
  
      self.input1_half_ = self.input1_half.clone()
      self.input2_half_ = self.input2_half.clone()
  
      self.input1_half.requires_grad_()
      self.input2_half.requires_grad_()
      self.input1_half_.requires_grad_()
      self.input2_half_.requires_grad_()
  
file_path: apex/apex/contrib/test/index_mul_2d/test_index_mul_2d.py
imports: import random
  import unittest
  import torch
label_desc: Use image node_id 1 for calling the IndexMul2dTest obj's underlying member method code with example usage: obj.setUp(seed) without return types