def forward(self, x):
        """Calculate embeddings or probabilities given a batch of input image tensors.

        Arguments:
            x {torch.tensor} -- Batch of image tensors representing faces.

        Returns:
            torch.tensor -- Batch of embeddings or softmax probabilities.
        """
        x = self.conv2d_1a(x)
        x = self.conv2d_2a(x)
        x = self.conv2d_2b(x)
        x = self.maxpool_3a(x)
        x = self.conv2d_3b(x)
        x = self.conv2d_4a(x)
        x = self.conv2d_4b(x)
        x = self.repeat_1(x)
        x = self.mixed_6a(x)
        x = self.repeat_2(x)
        x = self.mixed_7a(x)
        x = self.repeat_3(x)
        x = self.block8(x)
        x = self.avgpool_1a(x)
        x = self.dropout(x)
        x = self.last_linear(x.view(x.shape[0], -1))
        x = self.last_bn(x)
        x = F.normalize(x, p=2, dim=1)
        if self.classify:
            x = self.logits(x)
        return x
Calculate embeddings or probabilities given a batch of input image tensors.

        Arguments:
            x {torch.tensor} -- Batch of image tensors representing faces.

        Returns:
            torch.tensor -- Batch of embeddings or softmax probabilities.
        
models/inception_resnet_v1.py