Extracting specific blocks from a module list
I'm using a pretrained model in which there are several self_attentions sequentially stacked each one after another and the number of them is 12. I need to extract the output of the fourth and 10th blocks of this sequential layers. In the following script, the BLock represents each self-attention layer:
dpr = [x.item() for x in torch.linspace(0, 0.1, 12)]  # stochastic depth decay rule
    
    self.blocks = nn.ModuleList([
        Block(
            dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, attention_type=self.attention_type)
        for i in range(12)])

The self-attention layers (stack of Block) are as follows:
## Attention blocks
    for blk in self.blocks:
        x = blk(x, B, T, W)

How can I extract the fourth and the 10th layers' output?

To extract the output of a layer, you'll need to use hooks. A forward hook is a function that is called after the forward method of the module was executed.
Here's an example of how to do it:
vit = model(...)  # your model with 12 transformer blocks

features = {l:[] for l in range(len(vit.blocks))}  # place holder for the extracted features

def make_hook_function(layer):

  def hook(module, input, output):
    features[layer].appned(output)  # save the output of the layer to the place holder

  return hook

# place the hooks on the layers that interest you
vit.blocks[4].register_forward_hook(make_hook_function(4))
vit.blocks[10].register_forward_hook(make_hook_function(10))

pred = vit(x)  # run an image through the model

# now you can inspect features[4] and features[10]

A very comprehensive example of ViT feature extractor can be found here.
