code: def minimum(x1, x2, out=None, where=None, **kwargs):
      """
      Element-wise minimum of tensor elements.
  
      Compare two tensors and returns a new tensor containing the element-wise
      minima. If one of the elements being compared is a NaN, then that
      element is returned. If both elements are NaNs then the first is
      returned. The latter distinction is important for complex NaNs, which
      are defined as at least one of the real or imaginary parts being a NaN.
      The net effect is that NaNs are propagated.
  
      Parameters
      ----------
      x1, x2 : array_like
          The tensors holding the elements to be compared. They must have
          the same shape, or shapes that can be broadcast to a single shape.
      out : Tensor, None, or tuple of Tensor and None, optional
          A location into which the result is stored. If provided, it must have
          a shape that the inputs broadcast to. If not provided or `None`,
          a freshly-allocated tensor is returned. A tuple (possible only as a
          keyword argument) must have length equal to the number of outputs.
      where : array_like, optional
          Values of True indicate to calculate the ufunc at that position, values
          of False indicate to leave the value in the output alone.
      **kwargs
  
      Returns
      -------
      y : Tensor or scalar
          The minimum of `x1` and `x2`, element-wise.  Returns scalar if
          both  `x1` and `x2` are scalars.
  
      See Also
      --------
      maximum :
          Element-wise maximum of two tensors, propagates NaNs.
      fmin :
          Element-wise minimum of two tensors, ignores NaNs.
      amin :
          The minimum value of a tensor along a given axis, propagates NaNs.
      nanmin :
          The minimum value of a tenosr along a given axis, ignores NaNs.
  
      fmax, amax, nanmax
  
      Notes
      -----
      The minimum is equivalent to ``mt.where(x1 <= x2, x1, x2)`` when
      neither x1 nor x2 are NaNs, but it is faster and does proper
      broadcasting.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.minimum([2, 3, 4], [1, 5, 2]).execute()
      array([1, 3, 2])
  
      >>> mt.minimum(mt.eye(2), [0.5, 2]).execute() # broadcasting
      array([[ 0.5,  0. ],
             [ 0. ,  1. ]])
  
      >>> mt.minimum([mt.nan, 0, mt.nan],[0, mt.nan, mt.nan]).execute()
      array([ NaN,  NaN,  NaN])
      >>> mt.minimum(-mt.Inf, 1).execute()
      -inf
      """
      op = TensorMinimum(**kwargs)
      return op(x1, x2, out=out, where=where)
  
file_path: mars/mars/tensor/arithmetic/minimum.py
imports: import numpy
  from ...None import opcodes
  from ..utils import infer_dtype
  from .core import TensorBinOp
  from .utils import arithmetic_operand
label_desc: Use image node_id 1 for calling a global function with example usage: minimum(x1, x2, out, where) and returns: op