code: def nanprod(
      a: torch.Tensor,
      /,
      *,
      axis: Optional[Union[int, Sequence[int]]] = None,
      dtype: Optional[torch.dtype] = None,
      keepdims: Optional[bool] = False,
      out: Optional[torch.Tensor] = None,
      initial: Optional[
          Union[int, float, complex, ivy.Container]
      ] = None,
      where: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      dtype = ivy.as_native_dtype(dtype)
      if dtype is None:
          dtype = _infer_dtype(a.dtype)
      if initial is None:
          initial = 1
      a = a.type(dtype)
      a = torch.nan_to_num(a, nan=1.0)
      if a.dtype == torch.float16:
          a = a.type(torch.float32)
      if axis == ():
          return a.type(dtype)
      if axis is None:
          return torch.prod(input=a, out=out).type(dtype) * initial
      if isinstance(axis, (tuple, list)):
          for i in axis:
              a = torch.prod(a, dim=i, keepdim=keepdims, out=out).type(
                  dtype
              )
              if a.dtype == torch.float16:
                  a = a.type(torch.float32)
          return a.type(dtype) * initial
      return (
          torch.prod(a, dim=axis, keepdim=keepdims, out=out).type(dtype)
          * initial
      )
  
imports: from typing import Optional, Union, Tuple, Sequence
  import torch
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  import ivy
  from ..statistical import _infer_dtype
  from copy import deepcopy
label_desc: Use image node_id 5 for calling a global function with example usage: nanprod() and returns: unknown, a, unknown, unknown