sagemaker/src/handwriting_line_recognition.py [132:145]:
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        Returns
        -------
        network: gluon.nn.HybridSequential
            The body network for feature extraction based on resnet
        '''
        
        pretrained = resnet34_v1(pretrained=True, ctx=self.ctx)
        pretrained_2 = resnet34_v1(pretrained=True, ctx=mx.cpu(0))
        first_weights = pretrained_2.features[0].weight.data().mean(axis=1).expand_dims(axis=1)
        # First weights could be replaced with individual channels.
        
        body = gluon.nn.HybridSequential()
        with body.name_scope():
            first_layer = gluon.nn.Conv2D(channels=64, kernel_size=(7, 7), padding=(3, 3), strides=(2, 2), in_channels=1, use_bias=False)
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sagemaker/src/word_and_line_segmentation.py [73:86]:
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        Returns
        -------
        network: gluon.nn.HybridSequential
            The body network for feature extraction based on resnet
        
        '''
        pretrained = resnet34_v1(pretrained=True, ctx=self.ctx)
        pretrained_2 = resnet34_v1(pretrained=True, ctx=mx.cpu(0))
        first_weights = pretrained_2.features[0].weight.data().mean(axis=1).expand_dims(axis=1)
        # First weights could be replaced with individual channels.
        
        body = gluon.nn.HybridSequential()
        with body.name_scope():
            first_layer = gluon.nn.Conv2D(channels=64, kernel_size=(7, 7), padding=(3, 3), strides=(2, 2), in_channels=1, use_bias=False)
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