code: def count_nonzero(
      a: torch.Tensor,
      /,
      *,
      axis: Optional[Union[int, Tuple[int, ...]]] = None,
      keepdims: bool = False,
      dtype: Optional[torch.dtype] = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      if isinstance(axis, list):
          axis = tuple(axis)
      if dtype is None:
          x = torch.count_nonzero(a, dim=axis)
      else:
          x = torch.tensor(
              torch.count_nonzero(a, dim=axis), dtype=dtype
          )
      if not keepdims:
          return x
      if isinstance(axis, int):
          if axis == -1:
              temp = x.dim() - 1
              if temp < -1:
                  temp = 0
              return x.unsqueeze(temp)
          return x.unsqueeze(axis)
      elif axis is not None:
          for d in sorted(axis):
              x = x.unsqueeze(d)
          return x
      return x
  
imports: from typing import Optional, Union, Tuple, List, Sequence
  from numbers import Number
  import torch
  import ivy
  from ivy import promote_types_of_inputs
  from ivy.functional.backends.torch.elementwise import _cast_for_unary_op
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ..None import backend_version
label_desc: Use image node_id 8 for calling a global function with example usage: count_nonzero() and returns: x, x, x, x, x