code: def test_batcher(self):
      for _ in range(256):
          size = random.randint(1, 20)
          dim = random.randint(0, 2)
          dims = random.randint(dim + 1, dim + 2)
  
          n = random.randint(20, 100)
          shape = [random.randint(1, 4) for _ in range(dims)]
  
          batcher = moolib.Batcher(size=size, dim=dim)
  
          inputs = []
          for _ in range(n):
              input = torch.randn(shape)
              inputs.append(input.clone())
              batcher.stack(input)
              assert input.equal(inputs[-1])
              if not batcher.empty():
                  batched = batcher.get()
                  stacked = torch.stack(inputs, dim=dim)
                  assert batched.equal(stacked)
                  inputs = []
  
          batcher = moolib.Batcher(size=size, dim=dim)
  
          inputs = []
          for _ in range(n):
              input = torch.randn(shape)
              inputs.append(input.clone())
              batcher.cat(input)
              assert input.equal(inputs[-1])
              if not batcher.empty():
                  batched = batcher.get()
                  catted = torch.cat(inputs, dim=dim)
                  overflow = catted.narrow(
                      dim, size, catted.size(dim) - size
                  )
                  catted = catted.narrow(dim, 0, size)
                  assert batched.equal(catted)
                  inputs = []
                  if overflow.size(dim) > 0:
                      inputs.append(overflow)
  
imports: import random
  import torch
  import moolib
label_desc: Use image node_id 1 for calling the TestMoolibBatcher obj's underlying member method code with example usage: obj.test_batcher() without return types