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					@ -14,6 +14,7 @@
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					# ============================================================================
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					# ============================================================================
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					"""debug_ops"""
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					"""debug_ops"""
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					from ..._checkparam import ParamValidator as validator
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					from ...common import dtype as mstype
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					from ...common import dtype as mstype
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					from ..primitive import Primitive, prim_attr_register, PrimitiveWithInfer
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					from ..primitive import Primitive, prim_attr_register, PrimitiveWithInfer
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					@ -157,19 +158,20 @@ class InsertGradientOf(PrimitiveWithInfer):
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					class Print(PrimitiveWithInfer):
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					class Print(PrimitiveWithInfer):
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					    """
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					    """
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					    Output tensor to stdout.
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					    Output tensor or string to stdout.
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					    Inputs:
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					    Inputs:
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					        - **input_x** (Tensor) - The graph node to attach to.
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					        - **input_x** (Union[Tensor, str]) - The graph node to attach to. The input supports
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					          multiple strings and tensors which are separated by ','.
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					    Examples:
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					    Examples:
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					        >>> class PrintDemo(nn.Cell):
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					        >>> class PrintDemo(nn.Cell):
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					        >>>     def __init__(self,):
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					        >>>     def __init__(self):
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					        >>>         super(PrintDemo, self).__init__()
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					        >>>         super(PrintDemo, self).__init__()
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					        >>>         self.print = P.Print()
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					        >>>         self.print = P.Print()
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					        >>>
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					        >>>
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					        >>>     def construct(self, x):
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					        >>>     def construct(self, x, y):
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					        >>>         self.print(x)
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					        >>>         self.print('Print Tensor x and Tensor y:', x, y)
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					        >>>         return x
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					        >>>         return x
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					    """
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					    """
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					@ -181,4 +183,6 @@ class Print(PrimitiveWithInfer):
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					        return [1]
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					        return [1]
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					    def infer_dtype(self, *inputs):
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					    def infer_dtype(self, *inputs):
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					        for dtype in inputs:
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					            validator.check_subclass("input", dtype, (mstype.tensor, mstype.string))
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					        return mstype.int32
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					        return mstype.int32
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