(self, X_sample, y_sample, timestamps_sample, asset_names)
return X_sample, y_sample, timestamps_sample, asset_names
Transform.

        Parameters
        ----------
        X_sample : torch.Tensor
            Feature vector of shape `(n_channels, lookback, n_assets)`.

        y_sample : torch.Tesnor
            Target vector of shape `(n_channels, horizon, n_assets)`.

        timestamps_sample : datetime
            Time stamp of the sample.

        asset_names
            Asset names corresponding to the last channel of `X_sample` and `y_sample`.

        Returns
        -------
        X_sample_new : torch.Tensor
            Transformed version of `X_sample`.

        y_sample_new : torch.Tesnor
            Transformed version of `y_sample`.

        timestamps_sample_new : datetime
            Transformed version of `timestamps_sample`.

        asset_names_new
            Transformed version of `asset_names`.
Transform.
Transform .
def __call__(self, X_sample, y_sample, timestamps_sample, asset_names):
        """Transform.

        Parameters
        ----------
        X_sample : torch.Tensor
            Feature vector of shape `(n_channels, lookback, n_assets)`.

        y_sample : torch.Tesnor
            Target vector of shape `(n_channels, horizon, n_assets)`.

        timestamps_sample : datetime
            Time stamp of the sample.

        asset_names
            Asset names corresponding to the last channel of `X_sample` and `y_sample`.

        Returns
        -------
        X_sample_new : torch.Tensor
            Transformed version of `X_sample`.

        y_sample_new : torch.Tesnor
            Transformed version of `y_sample`.

        timestamps_sample_new : datetime
            Transformed version of `timestamps_sample`.

        asset_names_new
            Transformed version of `asset_names`.
        """
        for t in self.transforms:
            X_sample, y_sample, timestamps_sample, asset_names = t(X_sample, y_sample, timestamps_sample, asset_names)

        return X_sample, y_sample, timestamps_sample, asset_names
def __call__ ( self , X_sample , y_sample , timestamps_sample , asset_names ) : for t in self . transforms : X_sample , y_sample , timestamps_sample , asset_names = t ( X_sample , y_sample , timestamps_sample , asset_names ) return X_sample , y_sample , timestamps_sample , asset_names