[DEP] Remove update in BaseCollectionTransformer (#1894)

* remove Theta

* deprecate update

aeon/transformations/collection/base.py

@@ -29,7 +29,6 @@ class name: BaseCollectionTransformer
 
 import numpy as np
 import pandas as pd
-from deprecated.sphinx import deprecated
 
 from aeon.base import BaseCollectionEstimator
 from aeon.transformations.base import BaseTransformer
@@ -273,80 +272,6 @@ def inverse_transform(self, X, y=None):
 
         return Xt
 
-    # TODO: remove in v0.11.0
-    @deprecated(
-        version="0.10.0",
-        reason="The update method will be removed in version 0.11.0.",
-        category=FutureWarning,
-    )
-    @final
-    def update(self, X, y=None, update_params=True):
-        """Update transformer with X, optionally y.
-
-        State required:
-            Requires state to be "fitted".
-
-        Accesses in self:
-        _is_fitted : must be True
-        fitted model attributes (ending in "_") : must be set, accessed by _update
-
-        Writes to self:
-        _X : set to be X, if remember_data tag is True, potentially used in _update
-        fitted model attributes (ending in "_") : only if update_params=True
-            type and nature of update are dependent on estimator
-
-        Parameters
-        ----------
-        X : np.ndarray or list
-            Input data, any number of channels, equal length series of shape ``(
-            n_cases, n_channels, n_timepoints)``
-            or list of numpy arrays (any number of channels, unequal length series)
-            of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``,
-            where ``n_timepoints_i`` is length of series ``i``. Other types are
-            allowed and converted into one of the above.
-
-            Different estimators have different capabilities to handle different
-            types of input. If `self.get_tag("capability:multivariate")`` is False,
-            they cannot handle multivariate series. If ``self.get_tag(
-            "capability:unequal_length")`` is False, they cannot handle unequal
-            length input. In both situations, a ``ValueError`` is raised if X has a
-            characteristic that the estimator does not have the capability to handle.
-              Data to fit transform to, of valid collection type.
-        y : np.ndarray, default=None
-            1D np.array of float or str, of shape ``(n_cases)`` - class labels
-            (ground truth) for fitting indices corresponding to instance indices in X.
-            If None, no labels are used in fitting.
-        update_params : bool, default=True
-            whether the model is updated. Yes if true, if false, simply skips call.
-            argument exists for compatibility with forecasting module.
-
-        Returns
-        -------
-        self : a fitted instance of the estimator
-        """
-        # check whether is fitted
-        self.check_is_fitted()
-
-        # if requires_y is set, y is required in fit and update
-        if self.get_tag("requires_y") and y is None:
-            raise ValueError(f"{self.__class__.__name__} requires `y` in `update`.")
-
-        # check and convert X/y
-        X_inner = self._preprocess_collection(X, store_metadata=False)
-        y_inner = y
-
-        # update memory of X, if remember_data exists and is set to True
-        if self.get_tag("remember_data", tag_value_default=False):
-            self._X = X_inner
-
-        # skip everything if update_params is False or fit_is_empty is present and True
-        if not update_params or self.get_tag("fit_is_empty", tag_value_default=False):
-            return self
-
-        self._update(X=X_inner, y=y_inner)
-
-        return self
-
     def _fit(self, X, y=None):
         """Fit transformer to X and y.