How to parallelly process a tensor according to a list of segments?

I am attempting to process a tensor based on a list of segments. Each segment in the tensor will undergo a different operation. I have tried using the vmap function to solve this problem, but it is resulting in an error: “RuntimeError: vmap: It looks like you’re calling .item() on a Tensor.”. Here is a simplified version of my code:

import torch

segment_size = torch.randint(1, 6, size=(6,))
idx_end = torch.cumsum(segment_size, dim=0)
idx_start = torch.roll(idx_end, 1)
idx_start[0] = 0
idx_start_end =, idx_end.unsqueeze(0)), dim=0)
x = torch.randn(idx_end[-1])

def calculate(start_end, x):
    start = start_end[0]
    end = start_end[1]
    selected_x = x[start:end]
    selected_x = 0
    return selected_x

torch.vmap(calculate, in_dims=(1,None))(idx_start_end,x)

I would like to know how to resolve this issue or if there is a more efficient way to implement it.