…ent overflow (deepmodeling#4924)

The `numel` function in the Paddle backend was using `int` for computing
tensor element counts, which can overflow for large tensors. This fix
changes the return type and intermediate calculations to `size_t` to
handle larger tensor sizes safely.

## Problem

The original implementation multiplied tensor dimensions as `int`
values:

```cpp
int numel(const paddle_infer::Tensor& x) const {
  // TODO: There might be a overflow problem here for multiply int numbers.
  int ret = 1;
  std::vector<int> x_shape = x.shape();
  for (std::size_t i = 0, n = x_shape.size(); i < n; ++i) {
    ret *= x_shape[i];  // Can overflow for large tensors
  }
  return ret;
}
```

For large tensors (e.g., shape `[50000, 50000, 10]` = 25 billion
elements), this causes integer overflow and returns negative values.

## Solution

- Changed return type from `int` to `size_t`
- Changed intermediate calculations to use `size_t` with explicit
casting
- Updated all calling sites to use `size_t` variables
- Removed the TODO comment since the overflow issue is now resolved

```cpp
size_t numel(const paddle_infer::Tensor& x) const {
  size_t ret = 1;
  std::vector<int> x_shape = x.shape();
  for (std::size_t i = 0, n = x_shape.size(); i < n; ++i) {
    ret *= static_cast<size_t>(x_shape[i]);  // Safe from overflow
  }
  return ret;
}
```

The `size_t` type can handle up to 2^64 elements on 64-bit systems (vs
2^31 for `int`), making it appropriate for tensor element counts. This
change is backward compatible since `std::vector::resize()` and other
consumers already accept `size_t`.

Fixes deepmodeling#4551.

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