text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # Copyright 1999-2020 Alibaba Group Holding Ltd.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import numpy as np
  
  from ... import opcodes as OperandDef
  from ..datasource import tensor as astensor
  from .core import TensorReduction, TensorReductionMixin
  
  
  class TensorNanProd(TensorReduction, TensorReductionMixin):
      _op_type_ = OperandDef.NANPROD
      _func_name = 'nanprod'
  
      def __init__(self, axis=None, keepdims=None, combine_size=None, stage=None, **kw):
          stage = self._rewrite_stage(stage)
          super().__init__(_axis=axis, _keepdims=keepdims,
                           _combine_size=combine_size, stage=stage, **kw)
  
  
  def nanprod(a, axis=None, dtype=None, out=None, keepdims=None, combine_size=None):
      """
      Return the product of array elements over a given axis treating Not a
      Numbers (NaNs) as ones.
  
      One is returned for slices that are all-NaN or empty.
  
      Parameters
      ----------
      a : array_like
          Tensor containing numbers whose product is desired. If `a` is not an
          tensor, a conversion is attempted.
      axis : int, optional
          Axis along which the product is computed. The default is to compute
          the product of the flattened tensor.
      dtype : data-type, optional
          The type of the returned tensor and of the accumulator in which the
          elements are summed.  By default, the dtype of `a` is used.  An
          exception is when `a` has an integer type with less precision than
          the platform (u)intp. In that case, the default will be either
          (u)int32 or (u)int64 depending on whether the platform is 32 or 64
          bits. For inexact inputs, dtype must be inexact.
      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. The casting of NaN to integer can yield
          unexpected results.
      keepdims : bool, optional
          If 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 `arr`.
      combine_size: int, optional
          The number of chunks to combine.
  
      Returns
      -------
      nanprod : Tensor
          A new tensor holding the result is returned unless `out` is
          specified, in which case it is returned.
  
      See Also
      --------
      mt.prod : Product across array propagating NaNs.
      isnan : Show which elements are NaN.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.nanprod(1).execute()
      1
      >>> mt.nanprod([1]).execute()
      1
      >>> mt.nanprod([1, mt.nan]).execute()
      1.0
      >>> a = mt.array([[1, 2], [3, mt.nan]])
      >>> mt.nanprod(a).execute()
      6.0
      >>> mt.nanprod(a, axis=0).execute()
      array([ 3.,  2.])
  
      """
      a = astensor(a)
      if dtype is None:
          dtype = np.nanprod(np.empty((1,), dtype=a.dtype)).dtype
      op = TensorNanProd(axis=axis, dtype=dtype, keepdims=keepdims, combine_size=combine_size)
      return op(a, out=out)
  