code: def numpy_cat_impl(xs, dim):
      assert len(xs) > 0
      assert all(x.device == xs[0].device for x in xs)
      assert all(x.dtype == xs[0].dtype for x in xs)
      np_xs = [to_numpy(x) for x in xs]
      np_out = np.concatenate(np_xs, axis=dim)
      return torch.tensor(np_out, device=xs[0].device)
  
imports: import torch
  import functools
  from torch.testing import make_tensor
  from torch.testing._internal.opinfo.core import OpInfo, SampleInput
  from torch.testing._internal.common_dtype import all_types_and
  import numpy
  from torch.testing._internal.autograd_function_db import sample_inputs_numpy_cube, sample_inputs_numpy_mul, sample_inputs_numpy_sort, sample_inputs_numpy_take
  from torch import Tensor
  from torch.types import Number
  from typing import *
  import torch._custom_ops
label_desc: Use image node_id 33 for calling a global function with example usage: numpy_cat_impl(xs, dim) and returns: torch