raise ValueError("The value {} , its name '{}' already exists. " ValueError: The value Parameter (name=0.conv1.weight, shape=(128, 64, 3, 3), dtype=Float32, requires_grad=True) , its name '0.conv1.weight' already exists. Please set a unique name for the parameter.

import mindspore
from mindspore import nn
import mindspore.ops as ops

class Residual(nn.Cell):
    """The Residual block of ResNet."""
    def __init__(self, input_channels, num_channels,
                 use_1x1conv=False, strides=1):
        super().__init__()
        self.conv1 = nn.Conv2d(input_channels, num_channels,
                               kernel_size=3, padding=1, stride=strides, pad_mode='pad')
        self.conv2 = nn.Conv2d(num_channels, num_channels,
                               kernel_size=3, padding=1, pad_mode='pad')
        if use_1x1conv:
            self.conv3 = nn.Conv2d(input_channels, num_channels,
                                   kernel_size=1, stride=strides)
        else:
            self.conv3 = None
        self.bn1 = nn.BatchNorm2d(num_channels)
        self.bn2 = nn.BatchNorm2d(num_channels)

    def forward(self, X):
        Y = ops.relu(self.bn1(self.conv1(X)))
        Y = self.bn2(self.conv2(Y))
        if self.conv3:
            X = self.conv3(X)
        Y += X
        return ops.relu(Y)

def resnet_block(in_channels, out_channels, num_residuals,
                 first_block=False):
    blk = []
    for i in range(num_residuals):
        if i == 0 and not first_block:
            blk.append(Residual(in_channels, out_channels,
                                    use_1x1conv=True, strides=2))
            nn.Conv2d(in_channels, out_channels,
                      kernel_size=3, padding=1, stride=2, pad_mode='pad')
        else:
            blk.append(Residual(out_channels, out_channels))
    return nn.SequentialCell(*blk)

# net = resnet_block(10,3, 3)

net = nn.SequentialCell(
        nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, pad_mode='pad'),
        nn.BatchNorm2d(64),
        nn.ReLU())
net.insert_child_to_cell("resnet_block1", resnet_block(64, 64, 2, first_block=True))
net.insert_child_to_cell("resnet_block2", resnet_block(64, 128, 2))

trainer = nn.Adam(net.trainable_params(), learning_rate=0.001)