def sample_from_3dgrid(grid, coordinates):
    """
    Expects coordinates in shape (batch_size, num_points_per_batch, 3)
    Expects grid in shape (1, channels, H, W, D)
    (Also works if grid has batch size)
    Returns sampled features of shape (batch_size, num_points_per_batch, feature_channels)
    """
    batch_size, n_coords, n_dims = coordinates.shape
    sampled_features = torch.nn.functional.grid_sample(grid.expand(batch_size, -1, -1, -1, -1),
                                                       coordinates.reshape(batch_size, 1, 1, -1, n_dims),
                                                       mode='bilinear', padding_mode='zeros', align_corners=False)
    N, C, H, W, D = sampled_features.shape
    sampled_features = sampled_features.permute(0, 4, 3, 2, 1).reshape(N, H*W*D, C)
    return sampled_features

    Expects coordinates in shape (batch_size, num_points_per_batch, 3)
    Expects grid in shape (1, channels, H, W, D)
    (Also works if grid has batch size)
    Returns sampled features of shape (batch_size, num_points_per_batch, feature_channels)
    
training/volumetric_rendering/renderer.py