codecheck warning clean

mindspore/boost/base.py

@@ -135,6 +135,7 @@ class OptimizerProcess:
 
         return opt
 
+
 class ParameterProcess:
     """
     Process parameter for Boost. Currently, this class supports creating group parameters

mindspore/nn/loss/loss.py

@@ -28,6 +28,7 @@ from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
 from ... import context
 
+
 class LossBase(Cell):
     """
     Base class for other losses.
@@ -124,6 +125,7 @@ def _check_is_tensor(param_name, input_data, cls_name):
         raise TypeError(f"For '{cls_name}', the '{param_name}' should be '{mstype.tensor_type}', "
                         f"but got '{F.typeof(input_data)}'")
 
+
 class L1Loss(LossBase):
     r"""
     L1Loss creates a criterion to measure the mean absolute error (MAE) between :math:`x` and :math:`y` element-wise,
@@ -580,6 +582,7 @@ class SoftmaxCrossEntropyWithLogits(LossBase):
         x = self.softmax_cross_entropy(logits, labels)[0]
         return self.get_loss(x)
 
+
 @constexpr
 def _check_label_dtype(labels_dtype, cls_name):
     """Internal function, used to check whether the data type of labels meets the requirements."""

mindspore/run_check/run_check.py

@@ -19,6 +19,7 @@ mindspore.run_check
 The goal is to provide a convenient API to check if the installation is successful or failed.
 """
 
+
 def _check_mul():
     """
     Define the mul method.