import torch
from torch import nn

class FrozenBatchNorm2d(nn.Module):
    r"""
    BatchNorm2d where the batch statistics and the affine parameters are fixed

    Args:
        num_features (int): :math:`C` from an expected input of size :math:`(N, C, H, W)`

    Shape
        - Input: :math:`(N, C, H, W)`
        - Output: :math:`(N, C, H, W)` (same shape as input)
    """

    def __init__(self, num_features):
        super(FrozenBatchNorm2d, self).__init__()
        self.register_buffer("weight", torch.ones(num_features))
        self.register_buffer("bias", torch.zeros(num_features))
        self.register_buffer("running_mean", torch.zeros(num_features))
        self.register_buffer("running_var", torch.ones(num_features))

    def forward(self, input):
        return torch.ops.torch_ipex.frozen_batch_norm(input, self.weight, self.bias, self.running_mean, self.running_var)
