import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, a, b, gate):
        blended = gate * a + (1 - gate) * b
        return F.normalize(blended, p=2.0, dim=-1, eps=1e-12)

batch_size = 1024
dim = 1024

def get_inputs():
    a = torch.randn(batch_size, dim)
    b = torch.randn(batch_size, dim)
    gate = torch.rand(batch_size, dim)
    return [a, b, gate]

def get_init_inputs():
    return []