[ENH] Adds kdtw kernel support for kernelkmeans

[tanishy7777]

Reference Issues/PRs

Fixes #1728

What does this implement/fix? Explain your changes.

Adds support for Kdtw Kernel for KernelKmeans Clustering as implemented in https://github.com/pfmarteau/KDTW/tree/master and also the tests for the same

Changes and Explaination:
In the _fit method of TimeSeriesKernelKMeans before initializing the TsLearnKernelKMeans object we check if
self.kernel is equal to "kdtw" if thats the case we pass a callable function to self.kernel

def _fit(self, X, y=None):
    from tslearn.clustering import KernelKMeans as TsLearnKernelKMeans
    verbose = 0
    if self.verbose is True:
        verbose = 1
    
+   if self.kernel == "kdtw":
+     self.kernel = factory_kdtw_kernel(d=X.shape[1])
    
    self._tslearn_kernel_k_means = TsLearnKernelKMeans(
        n_clusters=self.n_clusters,
        kernel=self.kernel,
        max_iter=self.max_iter,
        tol=self.tol,
        n_init=self.n_init,
        kernel_params=self.kernel_params,
        n_jobs=self.n_jobs,
        verbose=verbose,
        random_state=self.random_state,
    )
   
    # further code

Apart from this we add 3 functions _kdtw_lk, kdtw and factory_kdtw_kernel

factory_kdtw_kernel: This returns a callable kdtw kernel function that unflattens the
inputs x and y which are univariate time series of dimension (n_timepoints*n_channels,)

kdtw: This is the kernel function for kdtw it needs the input x and y to be of dimension
(n_timepoints, n_channels,)

Tests are also added

Does your contribution introduce a new dependency? If yes, which one?

Any other comments?

Tests need to be added, but implementation is complete

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I have added the following labels to this PR based on the title: [ $\color{#FEF1BE}{\textsf{enhancement}}$ ].
I have added the following labels to this PR based on the changes made: [ $\color{#4011F3}{\textsf{clustering}}$ ]. Feel free to change these if they do not properly represent the PR.

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[chrisholder]

Another great addition thanks!