code: def nanmin(a, axis=None, out=None, keepdims=None, combine_size=None):
      """
      Return minimum of a tensor or minimum along an axis, ignoring any NaNs.
      When all-NaN slices are encountered a ``RuntimeWarning`` is raised and
      Nan is returned for that slice.
  
      Parameters
      ----------
      a : array_like
          Tensor containing numbers whose minimum is desired. If `a` is not an
          tensor, a conversion is attempted.
      axis : int, optional
          Axis along which the minimum is computed. The default is to compute
          the minimum of the flattened tensor.
      out : Tensor, optional
          Alternate output tensor in which to place the result.  The default
          is ``None``; if provided, it must have the same shape as the
          expected output, but the type will be cast if necessary.  See
          `doc.ufuncs` for details.
      keepdims : bool, optional
          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`.
  
          If the value is anything but the default, then
          `keepdims` will be passed through to the `min` method
          of sub-classes of `Tensor`.  If the sub-classes methods
          does not implement `keepdims` any exceptions will be raised.
      combine_size: int, optional
          The number of chunks to combine.
  
      Returns
      -------
      nanmin : Tensor
          An tensor with the same shape as `a`, with the specified axis
          removed.  If `a` is a 0-d tensor, or if axis is None, a tensor
          scalar is returned.  The same dtype as `a` is returned.
  
      See Also
      --------
      nanmax :
          The maximum value of an array along a given axis, ignoring any NaNs.
      amin :
          The minimum value of an array along a given axis, propagating any NaNs.
      fmin :
          Element-wise minimum of two arrays, ignoring any NaNs.
      minimum :
          Element-wise minimum of two arrays, propagating any NaNs.
      isnan :
          Shows which elements are Not a Number (NaN).
      isfinite:
          Shows which elements are neither NaN nor infinity.
  
      amax, fmax, maximum
  
      Notes
      -----
      Mars uses the IEEE Standard for Binary Floating-Point for Arithmetic
      (IEEE 754). This means that Not a Number is not equivalent to infinity.
      Positive infinity is treated as a very large number and negative
      infinity is treated as a very small (i.e. negative) number.
  
      If the input has a integer type the function is equivalent to mt.min.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> a = mt.array([[1, 2], [3, mt.nan]])
      >>> mt.nanmin(a).execute()
      1.0
      >>> mt.nanmin(a, axis=0).execute()
      array([ 1.,  2.])
      >>> mt.nanmin(a, axis=1).execute()
      array([ 1.,  3.])
  
      When positive infinity and negative infinity are present:
  
      >>> mt.nanmin([1, 2, mt.nan, mt.inf]).execute()
      1.0
      >>> mt.nanmin([1, 2, mt.nan, mt.NINF]).execute()
      -inf
  
      """
      a = astensor(a)
      op = TensorNanMin(
          axis=axis,
          dtype=a.dtype,
          keepdims=keepdims,
          combine_size=combine_size,
      )
      return op(a, out=out)
  
file_path: mars/mars/tensor/reduction/nanmin.py
imports: from ...None import opcodes
  from ..datasource import tensor
  from .core import TensorReduction, TensorReductionMixin
label_desc: Use image node_id 1 for calling a global function with example usage: nanmin(a, axis, out, keepdims, combine_size) and returns: op