Implement DelphiBERT model and data preparation scripts for tabular time series analysis

- Added `model.py` containing the DelphiBERT architecture, including TabularEncoder and AutoDiscretization classes for handling tabular features.
- Introduced `prepare_data.R` for merging disease and other data from UK Biobank, ensuring proper column selection and data integrity.
- Created `prepare_data.py` to process UK Biobank data, including mapping field IDs, handling date variables, and preparing tabular features and event data for model training.

age_encoder.py

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+import torch
+import torch.nn as nn
+
+
+class AgeSinusoidalEncoder(nn.Module):
+    """
+    Sinusoidal encoder for age.
+
+    Args:
+        n_embd (int): Embedding dimension. Must be even.
+    """
+
+    def __init__(self, n_embd: int):
+        super().__init__()
+        if n_embd % 2 != 0:
+            raise ValueError("n_embd must be even for sinusoidal encoding.")
+        self.n_embd = n_embd
+        i = torch.arange(0, self.n_embd, 2, dtype=torch.float32)
+        divisor = torch.pow(10000, i / self.n_embd)
+        self.register_buffer('divisor', divisor)
+
+    def forward(self, ages: torch.Tensor) -> torch.Tensor:
+        t_years = ages / 365.25
+        # Broadcast (B, L, 1) against (1, 1, D/2) to get (B, L, D/2)
+        args = t_years.unsqueeze(-1) / self.divisor.view(1, 1, -1)
+        # Interleave cos and sin along the last dimension
+        output = torch.zeros(
+            ages.shape[0], ages.shape[1], self.n_embd, device=ages.device)
+        output[:, :, 0::2] = torch.cos(args)
+        output[:, :, 1::2] = torch.sin(args)
+        return output
+
+
+class AgeMLPEncoder(nn.Module):
+    """
+    MLP encoder for age, using sinusoidal encoding as input.
+
+    Args:
+        n_embd (int): Embedding dimension.
+    """
+
+    def __init__(self, n_embd: int):
+        super().__init__()
+
+        self.sin_encoder = AgeSinusoidalEncoder(n_embd=n_embd)
+        self.mlp = nn.Sequential(
+            nn.Linear(n_embd, 4 * n_embd),
+            nn.GELU(),
+            nn.Linear(4 * n_embd, n_embd),
+        )
+
+    def forward(self, ages: torch.Tensor) -> torch.Tensor:
+        x = self.sin_encoder(ages)
+        output = self.mlp(x)
+        return output