[BUG] TransformerPipeline does not allow fitting

[helloplayer1]

Describe the bug

Trying to fit a pipeline including a KalmanFilterTransformerFP, TSInterpolator and an FCNRegressor with panel x data and a 1D numpy Array for y data produces an error.
To Reproduce

import numpy as np
import pandas as pd
from sktime.pipeline import make_pipeline
from sktime.transformations.series.kalman_filter import KalmanFilterTransformerFP
from sktime.transformations.compose import FitInTransform
from datetime import datetime
from sktime.transformations.panel.interpolate import TSInterpolator
from sklearn.model_selection import train_test_split
from sktime.regression.deep_learning import FCNRegressor

# Define the multi-index
index = pd.MultiIndex.from_tuples([
    (0, datetime.strptime('2024-04-20 18:22:14.877500', '%Y-%m-%d %H:%M:%S.%f')),
    (0, datetime.strptime('2024-04-20 18:22:14.903000', '%Y-%m-%d %H:%M:%S.%f')),
    (1, datetime.strptime('2024-04-20 18:24:42.453400', '%Y-%m-%d %H:%M:%S.%f')),
    (1, datetime.strptime('2024-04-20 18:24:42.478800', '%Y-%m-%d %H:%M:%S.%f'))
], names=['instance', 'Time'])

x_data = pd.DataFrame({
    'LeftControllerVelocity_0': [-0.01, -0.01, 0.06, 0.06]
}, index=index)
y_data = np.array([1,0.5]);

# Split the data into training and testing data
instances = x_data.index.get_level_values('instance').unique()
train_indicies, test_indicies = train_test_split(instances, test_size=0.3)

x_train = x_data.loc[train_indicies]
y_train = y_data[train_indicies]
y_test = y_data[test_indicies]
x_test = x_data.loc[test_indicies]

noise_filter = FitInTransform(KalmanFilterTransformerFP(1, denoising=True))
interpolator = TSInterpolator(4000)
regressor = FCNRegressor(verbose=True, n_epochs=80000)

model = make_pipeline(noise_filter, interpolator, regressor)

model.fit(x_train, y_train)

Expected behavior
Model is fitted.

Additional context
If you instead chain these estimators by yourself, it works, but only if you do not provide y_data for the fitting:

x_train = interpolator.fit_transform(noise_filter.fit_transform(x_train))
x_test = interpolator.transform(noise_filter.transform(x_test))
model = regressor

Versions

System: python: 3.11.0rc1 (main, Aug 12 2022, 10:02:14) [GCC 11.2.0] executable: /usr/bin/python machine: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.35

Python dependencies:
pip: 24.0
sktime: 0.29.0
sklearn: 1.4.2
skbase: 0.7.8
numpy: 1.26.4
scipy: 1.13.0
pandas: 2.2.2
matplotlib: 3.8.4
joblib: 1.4.2
numba: 0.59.1
statsmodels: 0.14.2
pmdarima: None
statsforecast: None
tsfresh: 0.20.2
tslearn: None
torch: None
tensorflow: 2.16.1
tensorflow_probability: None