PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs Frobenius norm normalization.
    """
    def __init__(self):
        """
        Initializes the Frobenius norm normalization layer.
        """
        super(Model, self).__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies Frobenius norm normalization to the input tensor.

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

        Returns:
            torch.Tensor: Output tensor with Frobenius norm normalization applied, same shape as input.
        """
        norm = torch.norm(x, p='fro')
        return x / norm

batch_size = 112
features = 64
dim1 = 512
dim2 = 512

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

def get_init_inputs():
    return []