torchrec/modules/crossnet.py [160:175]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                )
                for i in range(self._num_layers)
            ]
        )
        self.bias: torch.nn.Module = torch.nn.ParameterList(
            [
                torch.nn.Parameter(torch.nn.init.zeros_(torch.empty(in_features, 1)))
                for i in range(self._num_layers)
            ]
        )

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        x_0 = input.unsqueeze(2)  # (B, N, 1)
        x_l = x_0

        for layer in range(self._num_layers):
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torchrec/modules/crossnet.py [235:250]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                )
                for i in range(self._num_layers)
            ]
        )
        self.bias: torch.nn.Module = torch.nn.ParameterList(
            [
                torch.nn.Parameter(torch.nn.init.zeros_(torch.empty(in_features, 1)))
                for i in range(self._num_layers)
            ]
        )

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        x_0 = input.unsqueeze(2)  # (B, N, 1)
        x_l = x_0

        for layer in range(self._num_layers):
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