def calculate_receptive_fields(layers):
    """
    Calculate the receptive field for each layer given a list of (kernel size, stride) tuples.
    
    Parameters:
        layers (list of tuples): Each tuple contains (kernel size, stride) for a layer.
        
    Returns:
        list: Receptive field for each layer.
    """
    receptive_fields = []
    receptive_field = 1  # The initial receptive field for the first layer

    for i, (kernel_size, stride) in enumerate(layers):
        if i == 0:
            receptive_field = kernel_size
        else:
            receptive_field += (kernel_size - 1) * stride
        receptive_fields.append(receptive_field)
        
    return receptive_fields

from itertools import chain
from typing import List, List, Tuple
import torch
def get_feature_node_RF(blocks: List[List[Tuple[int, int]]], idxs_feature_node: List[int]):
    lens_block = [len(l) for l in blocks]
    idxs_block_out_layer = torch.cumsum(torch.tensor(lens_block), -1)-1
    idxs_layer_ft = idxs_block_out_layer[idxs_feature_node]
    layers_flatten = list(chain.from_iterable(blocks))
    rfs = calculate_receptive_fields(layers_flatten)
    return torch.tensor(rfs)[idxs_layer_ft]