--- a
+++ b/biovil_t/modules.py
@@ -0,0 +1,85 @@
+#  -------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  -------------------------------------------------------------------------------------------
+
+from typing import Callable, Optional
+
+import torch
+import torch.nn as nn
+
+
+class MLP(nn.Module):
+    """
+    Fully connected layers to map between image embeddings and projection space where pairs of images are compared.
+
+    :param input_dim: Input embedding feature size
+    :param hidden_dim: Hidden layer size in MLP
+    :param output_dim: Output projection size
+    :param use_1x1_convs: Use 1x1 conv kernels instead of 2D linear transformations for speed and memory efficiency.
+    """
+
+    def __init__(self,
+                 input_dim: int,
+                 output_dim: int,
+                 hidden_dim: Optional[int] = None,
+                 use_1x1_convs: bool = False) -> None:
+        super().__init__()
+
+        if use_1x1_convs:
+            linear_proj_1_args = {'in_channels': input_dim, 'out_channels': hidden_dim, 'kernel_size': 1, 'bias': False}
+            linear_proj_2_args = {'in_channels': hidden_dim, 'out_channels': output_dim, 'kernel_size': 1, 'bias': True}
+            normalisation_layer: Callable = nn.BatchNorm2d
+            projection_layer: Callable = nn.Conv2d
+        else:
+            linear_proj_1_args = {'in_features': input_dim, 'out_features': hidden_dim, 'bias': False}
+            linear_proj_2_args = {'in_features': hidden_dim, 'out_features': output_dim, 'bias': True}
+            normalisation_layer = nn.BatchNorm1d
+            projection_layer = nn.Linear
+
+        self.output_dim = output_dim
+        self.input_dim = input_dim
+        if hidden_dim is not None:
+            self.model = nn.Sequential(
+                projection_layer(**linear_proj_1_args),
+                normalisation_layer(hidden_dim),
+                nn.ReLU(inplace=True),
+                projection_layer(**linear_proj_2_args))
+        else:
+            self.model = nn.Linear(input_dim, output_dim)  # type: ignore
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """forward pass of the multi-layer perceptron"""
+        x = self.model(x)
+        return x
+
+
+class MultiTaskModel(nn.Module):
+    """Torch module for multi-task classification heads. We create a separate classification head
+    for each task and perform a forward pass on each head independently in forward(). Classification
+    heads are instances of `MLP`.
+
+    :param input_dim: Number of dimensions of the input feature map.
+    :param classifier_hidden_dim: Number of dimensions of hidden features in the MLP.
+    :param num_classes: Number of output classes per task.
+    :param num_tasks: Number of classification tasks or heads required.
+    """
+
+    def __init__(self, input_dim: int, classifier_hidden_dim: Optional[int], num_classes: int, num_tasks: int):
+
+        super().__init__()
+
+        self.num_classes = num_classes
+        self.num_tasks = num_tasks
+
+        for task in range(num_tasks):
+            setattr(self, "fc_" + str(task), MLP(input_dim, output_dim=num_classes, hidden_dim=classifier_hidden_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Returns [batch_size, num_tasks, num_classes] tensor of logits."""
+        batch_size = x.shape[0]
+        out = torch.zeros((batch_size, self.num_classes, self.num_tasks), dtype=x.dtype, device=x.device)
+        for task in range(self.num_tasks):
+            classifier = getattr(self, "fc_" + str(task))
+            out[:, :, task] = classifier(x)
+        return out