code: def nanmin(
      x: ivy.Array,
      /,
      *,
      axis: Optional[Union[Tuple[int], int]] = None,
      keepdims: Optional[bool] = False,
      out: Optional[ivy.Array] = None,
      initial: Optional[Union[int, float, complex]] = None,
      where: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """Return minimum of an array or minimum along an axis, ignoring any NaNs.
  
      Parameters
      ----------
      a
          Input array.
      axis
          Axis or axes along which the minimum is computed.
          The default is to compute the minimum of the flattened array.
      out
          optional output array, for writing the result to.
      keepdims
          If this is set to True, the axes which are reduced are left in the result
          as dimensions with size one. With this option, the result will broadcast
          correctly against the original a.
      initial
          The maximum value of an output element.
      where
          Elements to compare for the minimum
  
      Returns
      -------
      ret
          Return minimum of an array or minimum along an axis, ignoring any NaNs
  
      Functional Examples
      -------------------
      >>> a = ivy.array([[1, ivy.nan], [3, 4]])
      >>> ivy.nanmin(a)
      1.0
      >>> ivy.nanmin(a, axis=1)
      [1. 3.]
      >>> ivy.nanmin(a, axis=0, keepdims=True)
      [[1. 2.]]
      """
      return ivy.current_backend(x).nanmin(
          x,
          axis=axis,
          keepdims=keepdims,
          out=out,
          initial=initial,
          where=where,
      )
  
file_path: ivy/ivy/functional/ivy/experimental/statistical.py
imports: from typing import Optional, Union, Tuple, Sequence
  import ivy
  from ivy.func_wrapper import handle_array_function, handle_out_argument, to_native_arrays_and_back, handle_array_like_without_promotion, handle_nestable, infer_dtype, handle_device, handle_backend_invalid
  from ivy.utils.exceptions import handle_exceptions
label_desc: Use image node_id 4 for calling a global function with example usage: nanmin() and returns: ivy