PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs sum reduction over a specified dimension.
    """
    def __init__(self, dim: int):
        """
        Initializes the model with the dimension to reduce over.

        Args:
            dim (int): Dimension to reduce over.
        """
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies sum reduction over the specified dimension.

        Args:
            x (torch.Tensor): Input tensor of shape (..., dim, ...).

        Returns:
            torch.Tensor: Output tensor after sum reduction, shape (..., 1, ...).
        """
        return torch.sum(x, dim=self.dim, keepdim=True)

batch_size = 128
dim1 = 4096
dim2 = 4095
reduce_dim = 1

def get_inputs():
    x = torch.rand(batch_size, dim1, dim2)
    return [x]

def get_init_inputs():
    return [reduce_dim]