--- a
+++ b/4-Models/Inception-pytorch/Inception_Variants.py
@@ -0,0 +1,550 @@
+class Inception_Tabular_7_1(nn.Module):
+    def __init__(self, tabular_model, len_tabular_model_output, initial_kernel_num=64, initialize_parameters=False):
+        super(Inception_Tabular_7_1, self).__init__()
+
+        self.conv_1 = BasicConv2d(in_channels=1, out_channels=64, kernel_size=(7, 1), stride=(2, 1))
+
+        self.multi_2d_cnn_1a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=64, num_kernel=initial_kernel_num),
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+
+        self.multi_2d_cnn_1b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num * 1.5),
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 1.5),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+        self.multi_2d_cnn_1c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 2),
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 2),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+
+        self.multi_2d_cnn_2a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 4),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=597, num_kernel=initial_kernel_num * 5),
+          Multi_2D_CNN_block(in_channels=746, num_kernel=initial_kernel_num * 6),
+          Multi_2D_CNN_block(in_channels=896, num_kernel=initial_kernel_num * 7),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1045, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2d = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 12),
+          Multi_2D_CNN_block(in_channels=1792, num_kernel=initial_kernel_num * 14),
+          Multi_2D_CNN_block(in_channels=2090, num_kernel=initial_kernel_num * 16),
+        )
+        self.output_before_fc = nn.Sequential(
+          nn.AdaptiveAvgPool2d((1, 1)),
+          nn.Flatten(),
+          nn.Dropout(0.5),
+          # nn.Sigmoid()
+        )
+
+        self.tabular_model = tabular_model
+        self.len_tabular_model_output = len_tabular_model_output
+
+        # the extra features are for the output for the tabular data model
+        self.output = nn.Linear(2389 + len_tabular_model_output, 1)
+
+        if initialize_parameters:
+          for m in self.modules():
+            model_utils.initialize_parameters(m)
+
+      def forward(self, x_and_tabular):
+        """
+        Parameters
+        ----------
+        x_and_tabular tuple of (image input, tabular features)
+        where image input and tabular features are both tensors of size BATCHSIZE s.t.
+        BATCHSIZE is a batch size > 1.
+        Returns
+        -------
+        tensor of BATCHSIZE with forward pass results
+        """
+        x, tabular = x_and_tabular
+        x = self.conv_1(x)
+        # N x 1250 x 12 x 64 tensor
+        x = self.multi_2d_cnn_1a(x)
+        # N x 416 x 12 x 149 tensor
+        x = self.multi_2d_cnn_1b(x)
+        # N x 138 x 12 x 224 tensor
+        x = self.multi_2d_cnn_1c(x)
+        # N x 69 x 12 x 298
+        x = self.multi_2d_cnn_2a(x)
+
+        x = self.multi_2d_cnn_2b(x)
+
+        x = self.multi_2d_cnn_2c(x)
+
+        x = self.multi_2d_cnn_2d(x)
+
+        x = self.output_before_fc(x)
+
+        tabular_model_output = self.tabular_model(tabular)
+        len_tabular_model_output = tabular_model_output.shape[1]
+        assert len_tabular_model_output == self.len_tabular_model_output, \
+          f"Unexpected length {len_tabular_model_output}"
+        # see https://discuss.pytorch.org/t/concatenate-layer-output-with-additional-input-data/20462/2
+        x = torch.cat((x, tabular_model_output), dim=1)
+        x = self.output(x)
+
+        # WARNING: Be careful about .squeeze(). We expect for a given batch, the output is
+        # [BATCHSIZE, 1] and BATCHSIZE > 1. If the tensor has a batch dimension of size 1,
+        # then squeeze(input) will also unexpectedly remove the batch dimension!
+        return x.squeeze()
+
+
+class Inception_Tabular_15_3(nn.Module):
+
+    def __init__(self, tabular_model, len_tabular_model_output, initial_kernel_num=64, initialize_parameters=False):
+        super(Inception_Tabular_15_3, self).__init__()
+
+        self.conv_1 = BasicConv2d(in_channels=1, out_channels=64, kernel_size=(15, 3), stride=(2, 1))
+
+        self.multi_2d_cnn_1a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=64, num_kernel=initial_kernel_num),
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+
+        self.multi_2d_cnn_1b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num * 1.5),
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 1.5),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+        self.multi_2d_cnn_1c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 2),
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 2),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+
+        self.multi_2d_cnn_2a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 4),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=597, num_kernel=initial_kernel_num * 5),
+          Multi_2D_CNN_block(in_channels=746, num_kernel=initial_kernel_num * 6),
+          Multi_2D_CNN_block(in_channels=896, num_kernel=initial_kernel_num * 7),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1045, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2d = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 12),
+          Multi_2D_CNN_block(in_channels=1792, num_kernel=initial_kernel_num * 14),
+          Multi_2D_CNN_block(in_channels=2090, num_kernel=initial_kernel_num * 16),
+        )
+        self.output_before_fc = nn.Sequential(
+          nn.AdaptiveAvgPool2d((1, 1)),
+          nn.Flatten(),
+          nn.Dropout(0.5),
+          # nn.Sigmoid()
+        )
+
+        self.tabular_model = tabular_model
+        self.len_tabular_model_output = len_tabular_model_output
+
+        # the extra features are for the output for the tabular data model
+        self.output = nn.Linear(2389 + len_tabular_model_output, 1)
+
+        if initialize_parameters:
+          for m in self.modules():
+            model_utils.initialize_parameters(m)
+
+      def forward(self, x_and_tabular):
+        """
+        Parameters
+        ----------
+        x_and_tabular tuple of (image input, tabular features)
+        where image input and tabular features are both tensors of size BATCHSIZE s.t.
+        BATCHSIZE is a batch size > 1.
+        Returns
+        -------
+        tensor of BATCHSIZE with forward pass results
+        """
+        x, tabular = x_and_tabular
+        x = self.conv_1(x)
+        # N x 1250 x 12 x 64 tensor
+        x = self.multi_2d_cnn_1a(x)
+        # N x 416 x 12 x 149 tensor
+        x = self.multi_2d_cnn_1b(x)
+        # N x 138 x 12 x 224 tensor
+        x = self.multi_2d_cnn_1c(x)
+        # N x 69 x 12 x 298
+        x = self.multi_2d_cnn_2a(x)
+
+        x = self.multi_2d_cnn_2b(x)
+
+        x = self.multi_2d_cnn_2c(x)
+
+        x = self.multi_2d_cnn_2d(x)
+
+        x = self.output_before_fc(x)
+
+        tabular_model_output = self.tabular_model(tabular)
+        len_tabular_model_output = tabular_model_output.shape[1]
+        assert len_tabular_model_output == self.len_tabular_model_output, \
+          f"Unexpected length {len_tabular_model_output}"
+        # see https://discuss.pytorch.org/t/concatenate-layer-output-with-additional-input-data/20462/2
+        x = torch.cat((x, tabular_model_output), dim=1)
+        x = self.output(x)
+
+        # WARNING: Be careful about .squeeze(). We expect for a given batch, the output is
+        # [BATCHSIZE, 1] and BATCHSIZE > 1. If the tensor has a batch dimension of size 1,
+        # then squeeze(input) will also unexpectedly remove the batch dimension!
+        return x.squeeze()
+
+
+class Inception_Tabular_15_1(nn.Module):
+
+    def __init__(self, tabular_model, len_tabular_model_output, initial_kernel_num=64, initialize_parameters=False):
+        super(Inception_Tabular_15_1, self).__init__()
+
+        self.conv_1 = BasicConv2d(in_channels=1, out_channels=64, kernel_size=(15, 1), stride=(2, 1))
+
+        self.multi_2d_cnn_1a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=64, num_kernel=initial_kernel_num),
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+
+        self.multi_2d_cnn_1b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num * 1.5),
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 1.5),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+        self.multi_2d_cnn_1c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 2),
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 2),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+
+        self.multi_2d_cnn_2a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 4),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=597, num_kernel=initial_kernel_num * 5),
+          Multi_2D_CNN_block(in_channels=746, num_kernel=initial_kernel_num * 6),
+          Multi_2D_CNN_block(in_channels=896, num_kernel=initial_kernel_num * 7),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1045, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2d = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 12),
+          Multi_2D_CNN_block(in_channels=1792, num_kernel=initial_kernel_num * 14),
+          Multi_2D_CNN_block(in_channels=2090, num_kernel=initial_kernel_num * 16),
+        )
+        self.output_before_fc = nn.Sequential(
+          nn.AdaptiveAvgPool2d((1, 1)),
+          nn.Flatten(),
+          nn.Dropout(0.5),
+          # nn.Sigmoid()
+        )
+
+        self.tabular_model = tabular_model
+        self.len_tabular_model_output = len_tabular_model_output
+
+        # the extra features are for the output for the tabular data model
+        self.output = nn.Linear(2389 + len_tabular_model_output, 1)
+
+        if initialize_parameters:
+          for m in self.modules():
+            model_utils.initialize_parameters(m)
+
+      def forward(self, x_and_tabular):
+        """
+        Parameters
+        ----------
+        x_and_tabular tuple of (image input, tabular features)
+        where image input and tabular features are both tensors of size BATCHSIZE s.t.
+        BATCHSIZE is a batch size > 1.
+        Returns
+        -------
+        tensor of BATCHSIZE with forward pass results
+        """
+        x, tabular = x_and_tabular
+        x = self.conv_1(x)
+        # N x 1250 x 12 x 64 tensor
+        x = self.multi_2d_cnn_1a(x)
+        # N x 416 x 12 x 149 tensor
+        x = self.multi_2d_cnn_1b(x)
+        # N x 138 x 12 x 224 tensor
+        x = self.multi_2d_cnn_1c(x)
+        # N x 69 x 12 x 298
+        x = self.multi_2d_cnn_2a(x)
+
+        x = self.multi_2d_cnn_2b(x)
+
+        x = self.multi_2d_cnn_2c(x)
+
+        x = self.multi_2d_cnn_2d(x)
+
+        x = self.output_before_fc(x)
+
+        tabular_model_output = self.tabular_model(tabular)
+        len_tabular_model_output = tabular_model_output.shape[1]
+        assert len_tabular_model_output == self.len_tabular_model_output, \
+          f"Unexpected length {len_tabular_model_output}"
+        # see https://discuss.pytorch.org/t/concatenate-layer-output-with-additional-input-data/20462/2
+        x = torch.cat((x, tabular_model_output), dim=1)
+        x = self.output(x)
+
+        # WARNING: Be careful about .squeeze(). We expect for a given batch, the output is
+        # [BATCHSIZE, 1] and BATCHSIZE > 1. If the tensor has a batch dimension of size 1,
+        # then squeeze(input) will also unexpectedly remove the batch dimension!
+        return x.squeeze()
+
+
+class Inception_Tabular_5_Linears(nn.Module):
+
+    def __init__(self, tabular_model, len_tabular_model_output, initial_kernel_num=64, initialize_parameters=False):
+        
+        self.conv_1 = BasicConv2d(in_channels=1, out_channels=64, kernel_size=(7, 3), stride=(2, 1))
+
+        self.multi_2d_cnn_1a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=64, num_kernel=initial_kernel_num),
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+
+        self.multi_2d_cnn_1b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num * 1.5),
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 1.5),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+        self.multi_2d_cnn_1c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 2),
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 2),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+
+        self.multi_2d_cnn_2a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 4),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=597, num_kernel=initial_kernel_num * 5),
+          Multi_2D_CNN_block(in_channels=746, num_kernel=initial_kernel_num * 6),
+          Multi_2D_CNN_block(in_channels=896, num_kernel=initial_kernel_num * 7),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1045, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2d = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 12),
+          Multi_2D_CNN_block(in_channels=1792, num_kernel=initial_kernel_num * 14),
+          Multi_2D_CNN_block(in_channels=2090, num_kernel=initial_kernel_num * 16),
+        )
+        self.output_before_fc = nn.Sequential(
+          nn.AdaptiveAvgPool2d((1, 1)),
+          nn.Flatten(),
+          nn.Dropout(0.5),
+          # nn.Sigmoid()
+        )
+
+        self.tabular_model = tabular_model
+        self.len_tabular_model_output = len_tabular_model_output
+
+        # the extra features are for the output for the tabular data model
+        self.output = nn.Sequential(
+            nn.Linear(2389 + len_tabular_model_output, 1200),
+            nn.Linear(1200, 400),
+            nn.Linear(400, 100),
+            nn.Linear(100, 20),
+            nn.Linear(20, 1)
+            ),
+
+        if initialize_parameters:
+          for m in self.modules():
+            model_utils.initialize_parameters(m)
+
+      def forward(self, x_and_tabular):
+        """
+        Parameters
+        ----------
+        x_and_tabular tuple of (image input, tabular features)
+        where image input and tabular features are both tensors of size BATCHSIZE s.t.
+        BATCHSIZE is a batch size > 1.
+        Returns
+        -------
+        tensor of BATCHSIZE with forward pass results
+        """
+        x, tabular = x_and_tabular
+        x = self.conv_1(x)
+        # N x 1250 x 12 x 64 tensor
+        x = self.multi_2d_cnn_1a(x)
+        # N x 416 x 12 x 149 tensor
+        x = self.multi_2d_cnn_1b(x)
+        # N x 138 x 12 x 224 tensor
+        x = self.multi_2d_cnn_1c(x)
+        # N x 69 x 12 x 298
+        x = self.multi_2d_cnn_2a(x)
+
+        x = self.multi_2d_cnn_2b(x)
+
+        x = self.multi_2d_cnn_2c(x)
+
+        x = self.multi_2d_cnn_2d(x)
+
+        x = self.output_before_fc(x)
+
+        tabular_model_output = self.tabular_model(tabular)
+        len_tabular_model_output = tabular_model_output.shape[1]
+        assert len_tabular_model_output == self.len_tabular_model_output, \
+          f"Unexpected length {len_tabular_model_output}"
+        # see https://discuss.pytorch.org/t/concatenate-layer-output-with-additional-input-data/20462/2
+        x = torch.cat((x, tabular_model_output), dim=1)
+        x = self.output(x)
+
+        # WARNING: Be careful about .squeeze(). We expect for a given batch, the output is
+        # [BATCHSIZE, 1] and BATCHSIZE > 1. If the tensor has a batch dimension of size 1,
+        # then squeeze(input) will also unexpectedly remove the batch dimension!
+        return x.squeeze()
+
+class Inception_Tabular_5_Linears_BatchNorm(nn.Module):
+
+    def __init__(self, tabular_model, len_tabular_model_output, initial_kernel_num=64, initialize_parameters=False):
+        
+        self.conv_1 = BasicConv2d(in_channels=1, out_channels=64, kernel_size=(7, 3), stride=(2, 1))
+
+        self.multi_2d_cnn_1a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=64, num_kernel=initial_kernel_num),
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+
+        self.multi_2d_cnn_1b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=149, num_kernel=initial_kernel_num * 1.5),
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 1.5),
+          nn.MaxPool2d(kernel_size=(3, 1))
+        )
+        self.multi_2d_cnn_1c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=224, num_kernel=initial_kernel_num * 2),
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 2),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+
+        self.multi_2d_cnn_2a = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=298, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 3),
+          Multi_2D_CNN_block(in_channels=448, num_kernel=initial_kernel_num * 4),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2b = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=597, num_kernel=initial_kernel_num * 5),
+          Multi_2D_CNN_block(in_channels=746, num_kernel=initial_kernel_num * 6),
+          Multi_2D_CNN_block(in_channels=896, num_kernel=initial_kernel_num * 7),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2c = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1045, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 8),
+          nn.MaxPool2d(kernel_size=(2, 1))
+        )
+        self.multi_2d_cnn_2d = nn.Sequential(
+          Multi_2D_CNN_block(in_channels=1194, num_kernel=initial_kernel_num * 12),
+          Multi_2D_CNN_block(in_channels=1792, num_kernel=initial_kernel_num * 14),
+          Multi_2D_CNN_block(in_channels=2090, num_kernel=initial_kernel_num * 16),
+        )
+        self.output_before_fc = nn.Sequential(
+          nn.AdaptiveAvgPool2d((1, 1)),
+          nn.Flatten(),
+          nn.Dropout(0.5),
+          # nn.Sigmoid()
+        )
+
+        self.tabular_model = tabular_model
+        self.len_tabular_model_output = len_tabular_model_output
+
+        # the extra features are for the output for the tabular data model
+        self.output = nn.Sequential(
+            nn.Linear(2389 + len_tabular_model_output, 1200),
+            nn.BatchNorm1d(1200),
+            nn.Linear(1200, 400),
+            nn.BatchNorm1d(400),
+            nn.Linear(400, 100),
+            nn.BatchNorm1d(100),
+            nn.Linear(100, 20),
+            nn.BatchNorm1d(20),
+            nn.Linear(20, 1)
+            ),
+
+        if initialize_parameters:
+          for m in self.modules():
+            model_utils.initialize_parameters(m)
+
+      def forward(self, x_and_tabular):
+        """
+        Parameters
+        ----------
+        x_and_tabular tuple of (image input, tabular features)
+        where image input and tabular features are both tensors of size BATCHSIZE s.t.
+        BATCHSIZE is a batch size > 1.
+        Returns
+        -------
+        tensor of BATCHSIZE with forward pass results
+        """
+        x, tabular = x_and_tabular
+        x = self.conv_1(x)
+        # N x 1250 x 12 x 64 tensor
+        x = self.multi_2d_cnn_1a(x)
+        # N x 416 x 12 x 149 tensor
+        x = self.multi_2d_cnn_1b(x)
+        # N x 138 x 12 x 224 tensor
+        x = self.multi_2d_cnn_1c(x)
+        # N x 69 x 12 x 298
+        x = self.multi_2d_cnn_2a(x)
+
+        x = self.multi_2d_cnn_2b(x)
+
+        x = self.multi_2d_cnn_2c(x)
+
+        x = self.multi_2d_cnn_2d(x)
+
+        x = self.output_before_fc(x)
+
+        tabular_model_output = self.tabular_model(tabular)
+        len_tabular_model_output = tabular_model_output.shape[1]
+        assert len_tabular_model_output == self.len_tabular_model_output, \
+          f"Unexpected length {len_tabular_model_output}"
+        # see https://discuss.pytorch.org/t/concatenate-layer-output-with-additional-input-data/20462/2
+        x = torch.cat((x, tabular_model_output), dim=1)
+        x = self.output(x)
+
+        # WARNING: Be careful about .squeeze(). We expect for a given batch, the output is
+        # [BATCHSIZE, 1] and BATCHSIZE > 1. If the tensor has a batch dimension of size 1,
+        # then squeeze(input) will also unexpectedly remove the batch dimension!
+        return x.squeeze()