!8641 Updating notes on pynative examples of each class in nn_layer folder

From: @zhangz0911gm
Reviewed-by: 
Signed-off-by:

mindspore/nn/dynamic_lr.py

@@ -40,7 +40,8 @@ def piecewise_constant_lr(milestone, learning_rates):
     Examples:
         >>> milestone = [2, 5, 10]
         >>> learning_rates = [0.1, 0.05, 0.01]
-        >>> piecewise_constant_lr(milestone, learning_rates)
+        >>> output = piecewise_constant_lr(milestone, learning_rates)
+        >>> print(output)
         [0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01]
     """
     validator.check_value_type('milestone', milestone, (tuple, list))
@@ -100,7 +101,8 @@ def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch,
         >>> total_step = 6
         >>> step_per_epoch = 2
         >>> decay_epoch = 1
-        >>> exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
+        >>> output = exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
+        >>> print(output)
         [0.1, 0.1, 0.09000000000000001, 0.09000000000000001, 0.08100000000000002, 0.08100000000000002]
     """
     _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
@@ -142,7 +144,8 @@ def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch,
         >>> total_step = 6
         >>> step_per_epoch = 2
         >>> decay_epoch = 2
-        >>> natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
+        >>> output = natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
+        >>> print(output)
         [0.1, 0.1, 0.1, 0.1, 0.016529888822158657, 0.016529888822158657]
     """
     _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
@@ -185,7 +188,8 @@ def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, deca
         >>> total_step = 6
         >>> step_per_epoch = 1
         >>> decay_epoch = 1
-        >>> inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
+        >>> output = inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
+        >>> print(output)
         [0.1, 0.06666666666666667, 0.05, 0.04, 0.03333333333333333, 0.028571428571428574]
     """
     _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
@@ -227,7 +231,8 @@ def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
         >>> total_step = 6
         >>> step_per_epoch = 2
         >>> decay_epoch = 2
-        >>> cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
+        >>> output = cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
+        >>> print(output)
         [0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]
     """
     if not isinstance(min_lr, float):
@@ -295,7 +300,8 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e
         >>> step_per_epoch = 2
         >>> decay_epoch = 2
         >>> power = 0.5
-        >>> polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
+        >>> r = polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
+        >>> print(r)
         [0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01]
     """
     validator.check_positive_float(learning_rate, 'learning_rate')
@@ -350,7 +356,8 @@ def warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch):
         >>> total_step = 6
         >>> step_per_epoch = 2
         >>> warmup_epoch = 2
-        >>> warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
+        >>> output = warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
+        >>> print(output)
         [0.0, 0.0, 0.05, 0.05, 0.1, 0.1]
     """
     if not isinstance(learning_rate, float):

mindspore/nn/layer/activation.py

@@ -70,7 +70,8 @@ class Softmax(Cell):
     Examples:
         >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
         >>> softmax = nn.Softmax()
-        >>> softmax(input_x)
+        >>> output = softmax(input_x)
+        >>> print(output)
         [0.03168  0.01166  0.0861  0.636  0.2341]
     """
 
@@ -106,7 +107,8 @@ class LogSoftmax(Cell):
     Examples:
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> log_softmax = nn.LogSoftmax()
-        >>> log_softmax(input_x)
+        >>> output = log_softmax(input_x)
+        >>> print(output)
         [[-5.00672150e+00 -6.72150636e-03 -1.20067215e+01]
          [-7.00091219e+00 -1.40009127e+01 -9.12250078e-04]]
     """
@@ -174,7 +176,8 @@ class ReLU(Cell):
     Examples:
         >>> input_x = Tensor(np.array([-1, 2, -3, 2, -1]), mindspore.float16)
         >>> relu = nn.ReLU()
-        >>> relu(input_x)
+        >>> output = relu(input_x)
+        >>> print(output)
         [0.  2.  0.  2.  0.]
     """
 
@@ -203,7 +206,8 @@ class ReLU6(Cell):
     Examples:
         >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
         >>> relu6 = nn.ReLU6()
-        >>> relu6(input_x)
+        >>> output = relu6(input_x)
+        >>> print(output)
         [0.  0.  0.  2.  1.]
     """
 
@@ -240,7 +244,8 @@ class LeakyReLU(Cell):
     Examples:
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> leaky_relu = nn.LeakyReLU()
-        >>> leaky_relu(input_x)
+        >>> output = leaky_relu(input_x)
+        >>> print(output)
         [[-0.2  4.  -1.6]
          [ 2   -1.   9.]]
     """
@@ -284,7 +289,8 @@ class Tanh(Cell):
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3, 2, 1]), mindspore.float16)
         >>> tanh = nn.Tanh()
-        >>> tanh(input_x)
+        >>> output = tanh(input_x)
+        >>> print(output)
         [0.7617  0.964  0.995  0.964 0.7617]
     """
 
@@ -315,7 +321,8 @@ class GELU(Cell):
     Examples:
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> gelu = nn.GELU()
-        >>> gelu(input_x)
+        >>> output = gelu(input_x)
+        >>> print(output)
         [[-1.5880802e-01  3.9999299e+00 -3.1077917e-21]
          [ 1.9545976e+00 -2.2918017e-07  9.0000000e+00]]
     """
@@ -346,7 +353,8 @@ class Sigmoid(Cell):
     Examples:
         >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
         >>> sigmoid = nn.Sigmoid()
-        >>> sigmoid(input_x)
+        >>> output = sigmoid(input_x)
+        >>> print(output)
         [0.2688  0.11914  0.5  0.881  0.7305]
     """
 
@@ -384,7 +392,8 @@ class PReLU(Cell):
     Examples:
         >>> input_x = Tensor(np.array([[[[0.1, 0.6], [0.9, 0.9]]]]), mindspore.float32)
         >>> prelu = nn.PReLU()
-        >>> prelu(input_x)
+        >>> output = prelu(input_x)
+        >>> print(output)
         [[[[0.1 0.6]
            [0.9 0.9]]]]
 
@@ -506,6 +515,7 @@ class LogSigmoid(Cell):
         >>> net = nn.LogSigmoid()
         >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
         >>> logsigmoid = net(input_x)
+        >>> print(logsigmoid)
         [-3.1326166e-01, -1.2692806e-01, -4.8587345e-02]
 
     """

mindspore/nn/layer/basic.py

@@ -76,7 +76,8 @@ class Dropout(Cell):
         >>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32)
         >>> net = nn.Dropout(keep_prob=0.8)
         >>> net.set_train()
-        >>> net(x)
+        >>> output = net(x)
+        >>> print(output)
         [[[0., 1.25, 0.],
           [1.25, 1.25, 1.25]],
          [[1.25, 1.25, 1.25],
@@ -141,7 +142,8 @@ class Flatten(Cell):
     Examples:
         >>> input = Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32)
         >>> net = nn.Flatten()
-        >>> net(input)
+        >>> output = net(input)
+        >>> print(output)
         [[1.2 1.2 2.1 2.1]
          [2.2 2.2 3.2 3.2]]
     """
@@ -196,7 +198,8 @@ class Dense(Cell):
     Examples:
         >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
         >>> net = nn.Dense(3, 4)
-        >>> net(input)
+        >>> output = net(input)
+        >>> print(output)
         [[ 2.5246444   2.2738023   0.5711005  -3.9399147 ]
          [ 1.0739875   4.0155234   0.94188046 -5.459526  ]]
     """
@@ -317,7 +320,8 @@ class ClipByNorm(Cell):
         >>> net = nn.ClipByNorm()
         >>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
         >>> clip_norm = Tensor(np.array([100]).astype(np.float32))
-        >>> net(input, clip_norm).shape
+        >>> result = net(input, clip_norm).shape
+        >>> print(result)
         (4, 16)
 
     """
@@ -386,7 +390,8 @@ class Norm(Cell):
     Examples:
         >>> net = nn.Norm(axis=0)
         >>> input = Tensor(np.random.randint(0, 10, [2, 4]), mindspore.float32)
-        >>> net(input)
+        >>> output = net(input)
+        >>> print(output)
         [2.236068 9.848858 4. 5.656854]
     """
 
@@ -442,7 +447,8 @@ class OneHot(Cell):
     Examples:
         >>> net = nn.OneHot(depth=4, axis=1)
         >>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
-        >>> net(indices)
+        >>> output = net(indices)
+        >>> print(output)
         [[[0. 0.]
           [1. 0.]
           [0. 0.]
@@ -501,11 +507,11 @@ class Pad(Cell):
         >>> import mindspore.nn as nn
         >>> import numpy as np
         >>> class Net(nn.Cell):
-        >>>     def __init__(self):
-        >>>         super(Net, self).__init__()
-        >>>         self.pad = nn.Pad(paddings=((1,1),(2,2)), mode="CONSTANT")
-        >>>     def construct(self, x):
-        >>>         return self.pad(x)
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self.pad = nn.Pad(paddings=((1,1),(2,2)), mode="CONSTANT")
+        ...     def construct(self, x):
+        ...         return self.pad(x)
         >>> x = np.random.random(size=(2, 3)).astype(np.float32)
         >>> pad = Net()
         >>> ms_output = pad(Tensor(x))
@@ -567,9 +573,10 @@ class Unfold(Cell):
     Examples:
         >>> net = Unfold(ksizes=[1, 2, 2, 1], strides=[1, 2, 2, 1], rates=[1, 2, 2, 1])
         >>> image = Tensor(np.ones([2, 3, 6, 6]), dtype=mstype.float16)
-        >>> net(image)
-        Tensor ([[[[1, 1] [1, 1]] [[1, 1], [1, 1]] [[1, 1] [1, 1]], [[1, 1] [1, 1]], [[1, 1] [1, 1]],
-                [[1, 1], [1, 1]]]], shape=(2, 12, 2, 2), dtype=mstype.float16)
+        >>> output = net(image)
+        >>> print(output)
+        [[[[1, 1] [1, 1]] [[1, 1], [1, 1]] [[1, 1] [1, 1]], [[1, 1] [1, 1]], [[1, 1] [1, 1]],
+                [[1, 1], [1, 1]]]]
     """
 
     def __init__(self, ksizes, strides, rates, padding="valid"):
@@ -621,6 +628,7 @@ class MatrixDiag(Cell):
         >>> x = Tensor(np.array([1, -1]), mstype.float32)
         >>> matrix_diag = nn.MatrixDiag()
         >>> result = matrix_diag(x)
+        >>> print(result)
         [[1.   0.]
          [0.  -1.]]
     """
@@ -652,6 +660,7 @@ class MatrixDiagPart(Cell):
         >>> x = Tensor([[[-1, 0], [0, 1]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32)
         >>> matrix_diag_part = nn.MatrixDiagPart()
         >>> result = matrix_diag_part(x)
+        >>> print(result)
         [[-1., 1.], [-1., 1.], [-1., 1.]]
     """
     def __init__(self):
@@ -684,6 +693,7 @@ class MatrixSetDiag(Cell):
         >>> diagonal = Tensor([[-1., 2.], [-1., 1.], [-1., 1.]], mindspore.float32)
         >>> matrix_set_diag = nn.MatrixSetDiag()
         >>> result = matrix_set_diag(x, diagonal)
+        >>> print(result)
         [[[-1, 0], [0, 2]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]]
     """
     def __init__(self):

mindspore/nn/layer/combined.py

@@ -80,7 +80,8 @@ class Conv2dBnAct(Cell):
         >>> net = nn.Conv2dBnAct(120, 240, 4, has_bn=True, activation='relu')
         >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
         >>> result = net(input)
-        >>> result.shape
+        >>> output = result.shape
+        >>> print(output)
         (1, 240, 1024, 640)
     """
 
@@ -171,7 +172,8 @@ class DenseBnAct(Cell):
         >>> net = nn.DenseBnAct(3, 4)
         >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
         >>> result = net(input)
-        >>> result.shape
+        >>> output = result.shape
+        >>> print(output)
         (2, 4)
     """
 

mindspore/nn/layer/container.py

@@ -87,7 +87,8 @@ class SequentialCell(Cell):
         >>> seq = nn.SequentialCell([conv, bn, relu])
         >>>
         >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
-        >>> seq(x)
+        >>> output = seq(x)
+        >>> print(output)
         [[[[0.02531557 0.        ]
            [0.04933941 0.04880078]]
           [[0.         0.        ]
@@ -155,7 +156,8 @@ class SequentialCell(Cell):
             >>> seq = nn.SequentialCell([conv, bn])
             >>> seq.append(relu)
             >>> x = Tensor(np.ones([1, 3, 4, 4]), dtype=mindspore.float32)
-            >>> seq(x)
+            >>> output = seq(x)
+            >>> print(output)
             [[[[0.12445523 0.12445523]
                [0.12445523 0.12445523]]
               [[0.         0.        ]

mindspore/nn/layer/conv.py

@@ -199,7 +199,8 @@ class Conv2d(_Conv):
     Examples:
         >>> net = nn.Conv2d(120, 240, 4, has_bias=False, weight_init='normal')
         >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
-        >>> net(input).shape
+        >>> output = net(input).shape
+        >>> print(output)
         (1, 240, 1024, 640)
     """
 
@@ -374,7 +375,8 @@ class Conv1d(_Conv):
     Examples:
         >>> net = nn.Conv1d(120, 240, 4, has_bias=False, weight_init='normal')
         >>> input = Tensor(np.ones([1, 120, 640]), mindspore.float32)
-        >>> net(input).shape
+        >>> output = net(input).shape
+        >>> print(output)
         (1, 240, 640)
     """
 
@@ -544,7 +546,8 @@ class Conv2dTranspose(_Conv):
     Examples:
         >>> net = nn.Conv2dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad')
         >>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32)
-        >>> net(input).shape
+        >>> output = net(input).shape
+        >>> print(output)
         (1, 64, 19, 53)
         """
 
@@ -719,7 +722,8 @@ class Conv1dTranspose(_Conv):
     Examples:
         >>> net = nn.Conv1dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad')
         >>> input = Tensor(np.ones([1, 3, 50]), mindspore.float32)
-        >>> net(input).shape
+        >>> output = net(input).shape
+        >>> print(output)
         (1, 64, 53)
     """
 

mindspore/nn/layer/embedding.py

@@ -66,7 +66,8 @@ class Embedding(Cell):
         >>>
         >>> # Maps the input word IDs to word embedding.
         >>> output = net(input_data)
-        >>> output.shape
+        >>> result = output.shape
+        >>> print(result)
         (8, 128, 768)
     """
 

mindspore/nn/layer/image.py

@@ -53,7 +53,8 @@ class ImageGradients(Cell):
     Examples:
         >>> net = nn.ImageGradients()
         >>> image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
-        >>> net(image)
+        >>> output = net(image)
+        >>> print(output)
         [[[[2,2]
            [0,0]]]]
         [[[[1,0]
@@ -214,6 +215,7 @@ class SSIM(Cell):
         >>> img1 = Tensor(np.random.random((1,3,16,16)), mindspore.float32)
         >>> img2 = Tensor(np.random.random((1,3,16,16)), mindspore.float32)
         >>> ssim = net(img1, img2)
+        >>> print(ssim)
         [0.12174469]
     """
     def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
@@ -379,6 +381,7 @@ class PSNR(Cell):
         >>> img1 = Tensor(np.random.random((1,3,16,16)))
         >>> img2 = Tensor(np.random.random((1,3,16,16)))
         >>> psnr = net(img1, img2)
+        >>> print(psnr)
         [7.8297315]
     """
     def __init__(self, max_val=1.0):
@@ -447,7 +450,8 @@ class CentralCrop(Cell):
         >>> net = nn.CentralCrop(central_fraction=0.5)
         >>> image = Tensor(np.random.random((4, 3, 4, 4)), mindspore.float32)
         >>> output = net(image)
-        >>> output.shape
+        >>> result = output.shape
+        >>> print(result)
         (4, 3, 2, 2)
     """
 

mindspore/nn/layer/math.py

@@ -64,7 +64,8 @@ class ReduceLogSumExp(Cell):
         >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = nn.ReduceLogSumExp(1, keep_dims=True)
         >>> output = op(input_x)
-        >>> output.shape
+        >>> result = output.shape
+        >>> print(reuslt)
         (3, 1, 5, 6)
     """
 
@@ -101,6 +102,7 @@ class Range(Cell):
     Examples:
         >>> net = nn.Range(1, 8, 2)
         >>> out = net()
+        >>> print(out)
         [1, 3, 5, 7]
     """
 
@@ -154,6 +156,7 @@ class LinSpace(Cell):
     Examples:
         >>> linspace = nn.LinSpace(1, 10, 5)
         >>> output = linspace()
+        >>> print(output)
         [1, 3.25, 5.5, 7.75, 10]
     """
 

mindspore/nn/layer/normalization.py

@@ -522,7 +522,8 @@ class LayerNorm(Cell):
         >>> x = Tensor(np.ones([20, 5, 10, 10]), mindspore.float32)
         >>> shape1 = x.shape[1:]
         >>> m = nn.LayerNorm(shape1,  begin_norm_axis=1, begin_params_axis=1)
-        >>> m(x).shape
+        >>> output = m(x).shape
+        >>> print(output)
         (20, 5, 10, 10)
     """
 
@@ -593,7 +594,8 @@ class GroupNorm(Cell):
     Examples:
         >>> goup_norm_op = nn.GroupNorm(2, 2)
         >>> x = Tensor(np.ones([1, 2, 4, 4], np.float32))
-        >>> goup_norm_op(x)
+        >>> output = goup_norm_op(x)
+        >>> print(output)
         [[[[0. 0. 0. 0.]
            [0. 0. 0. 0.]
            [0. 0. 0. 0.]

mindspore/nn/layer/pooling.py

@@ -107,6 +107,7 @@ class MaxPool2d(_PoolNd):
     Examples:
         >>> pool = nn.MaxPool2d(kernel_size=3, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
+        >>> print(x)
         [[[[1. 5. 5. 1.]
            [0. 3. 4. 8.]
            [4. 2. 7. 6.]
@@ -116,9 +117,10 @@ class MaxPool2d(_PoolNd):
            [0. 0. 4. 0.]
            [1. 8. 7. 0.]]]]
         >>> output = pool(x)
-        >>> output.shape
+        >>> reuslt = output.shape
+        >>> print(result)
         (1, 2, 2, 2)
-        >>> output
+        >>> print(output)
         [[[[7. 8.]
            [9. 9.]]
           [[7. 8.]
@@ -185,7 +187,8 @@ class MaxPool1d(_PoolNd):
         >>> max_pool = nn.MaxPool1d(kernel_size=3, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4]), mindspore.float32)
         >>> output = max_pool(x)
-        >>> output.shape
+        >>> result = output.shape
+        >>> printI(result)
         (1, 2, 2)
     """
 
@@ -269,6 +272,7 @@ class AvgPool2d(_PoolNd):
     Examples:
         >>> pool = nn.AvgPool2d(kernel_size=3, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
+        >>> print(x)
         [[[[5. 5. 9. 9.]
             [8. 4. 3. 0.]
             [2. 7. 1. 2.]
@@ -278,9 +282,10 @@ class AvgPool2d(_PoolNd):
             [0. 8. 9. 7.]
             [2. 1. 4. 9.]]]]
         >>> output = pool(x)
-        >>> output.shape
+        >>> result  = output.shape
+        >>> print(result)
         (1, 2, 2, 2)
-        >>> output
+        >>> print(output)
         [[[[4.888889  4.4444447]
            [4.111111  3.4444444]]
           [[4.2222223 4.5555553]
@@ -345,7 +350,8 @@ class AvgPool1d(_PoolNd):
         >>> pool = nn.AvgPool1d(kernel_size=6, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 3, 6]), mindspore.float32)
         >>> output = pool(x)
-        >>> output.shape
+        >>> result = output.shape
+        >>> print(result)
         (1, 3, 1)
     """
 

mindspore/nn/layer/quant.py

@@ -116,7 +116,8 @@ def _partial_init(cls_or_self, **kwargs):
         >>> foo_builder = Foo.partial_init(a=3, b=4).partial_init(answer=42)
         >>> foo_instance1 = foo_builder()
         >>> foo_instance2 = foo_builder()
-        >>> id(foo_instance1) == id(foo_instance2)
+        >>> result = (id(foo_instance1) == id(foo_instance2))
+        >>> print(result)
         False
     """
 
@@ -234,7 +235,7 @@ class FakeQuantWithMinMaxObserver(UniformQuantObserver):
         >>> fake_quant = nn.FakeQuantWithMinMaxObserver()
         >>> input = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
         >>> result = fake_quant(input)
-        >>> result
+        >>> print(result)
         [[0.9882355, 1.9764705, 0.9882355], [-1.9764705, 0. , -0.9882355]]
     """
 
@@ -591,7 +592,8 @@ class Conv2dBnFoldQuant(Cell):
         >>>                                      quant_config=qconfig)
         >>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mindspore.float32)
         >>> result = conv2d_bnfold(input)
-        >>> result.shape
+        >>> output = result.shape
+        >>> print(output)
         (2, 6, 2, 2)
     """
 
@@ -776,7 +778,8 @@ class Conv2dBnWithoutFoldQuant(Cell):
         >>>                                                quant_config=qconfig)
         >>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mstype.float32)
         >>> result = conv2d_no_bnfold(input)
-        >>> result.shape
+        >>> output = result.shape
+        >>> print(output)
         (2, 6, 2, 2)
     """
 
@@ -897,7 +900,8 @@ class Conv2dQuant(Cell):
         >>>                               quant_config=qconfig)
         >>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mindspore.float32)
         >>> result = conv2d_quant(input)
-        >>> result.shape
+        >>> output = result.shape
+        >>> print(output)
         (2, 6, 2, 2)
     """
 
@@ -997,7 +1001,8 @@ class DenseQuant(Cell):
         >>> dense_quant = nn.DenseQuant(3, 6, quant_config=qconfig)
         >>> input = Tensor(np.random.randint(-2, 2, (2, 3)), mindspore.float32)
         >>> result = dense_quant(input)
-        >>> result.shape
+        >>> output = result.shape
+        >>> print(output)
         (2, 6)
     """
 
@@ -1102,7 +1107,7 @@ class ActQuant(_QuantActivation):
         >>> act_quant = nn.ActQuant(nn.ReLU(), quant_config=qconfig)
         >>> input = Tensor(np.array([[1, 2, -1], [-2, 0, -1]]), mindspore.float32)
         >>> result = act_quant(input)
-        >>> result
+        >>> print(result)
         [[0.9882355, 1.9764705, 0.], [0., 0., 0.]]
     """
 
@@ -1164,7 +1169,7 @@ class TensorAddQuant(Cell):
         >>> input_x1 = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
         >>> input_x2 = Tensor(np.ones((2, 3)), mindspore.float32)
         >>> result = add_quant(input_x1, input_x2)
-        >>> result
+        >>> print(result)
         [[1.9764705, 3.011765, 1.9764705], [-0.9882355, 0.9882355, 0.]]
     """
 
@@ -1211,7 +1216,7 @@ class MulQuant(Cell):
         >>> input_x1 = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
         >>> input_x2 = Tensor(np.ones((2, 3)) * 2, mindspore.float32)
         >>> result = mul_quant(input_x1, input_x2)
-        >>> result
+        >>> print(result)
         [[1.9764705, 4.0000005, 1.9764705], [-4., 0., -1.9764705]]
     """