I have a model code that defined as (model_baseline)
        modules = []
        for block in blocks:
            for bottleneck in block:
                modules.append(
                    unit_module(bottleneck.in_channel,
                                bottleneck.depth,
                                bottleneck.stride))
        self.body = Sequential(*modules)

        self._initialize_weights()

    def forward(self, x):
        x = self.input_layer(x)
        x = self.body(x)
        x = self.output_layer(x)

        return x

Link: https://github.com/ZhaoJ9014/face.evoLVe.PyTorch/blob/d5e31893f7e30c0f82262e701463fd83d9725381/backbone/model_irse.py#L156
where blocks is
blocks = [
            get_block(in_channel=64, depth=64, num_units=3),
            get_block(in_channel=64, depth=128, num_units=13),
            get_block(in_channel=128, depth=256, num_units=30),
            get_block(in_channel=256, depth=512, num_units=3)
        ]

I want to access the output of block 0 in the body during training (get_block(in_channel=64, depth=64, num_units=13),) . How can I do it in pytorch? Note that, the network is loaded from a pretrained model
This is what I tried
        modules_0 = []
        modules = []
        for block in blocks[0:1]:
            for bottleneck in block:
                modules_0.append(
                    unit_module(bottleneck.in_channel,
                                bottleneck.depth,
                                bottleneck.stride))
        self.body_0 = Sequential(*modules_0)

        modules = []
        for block in blocks[1:]:
            for bottleneck in block:
                modules.append(
                    unit_module(bottleneck.in_channel,
                                bottleneck.depth,
                                bottleneck.stride))
        self.body = Sequential(*modules)


        self._initialize_weights()

    def forward(self, x):
        x = self.input_layer(x)
        x = self.body_0(x)
        x = self.body(x)
        x = self.output_layer(x)

However, the above method cannot use weight from pretrained model that trained on the model_baseline architecture