No real need to add values after performing train_test_split as the output is an array itself. Simply try with:

Xtrain = Xtrain.reshape(-1,1)
Xtest = Xtest.reshape(-1,1)

This seems like an inconsistency in the TensorFlow API, since almost all other op functions accept NumPy arrays wherever a tf.Tensor is expected. I've filed an issue to track the fix.

Fortunately, there is a simple workaround, using tf.convert_to_tensor(). Replace your code with the following:

flipped_images = tf.image.random_flip_left_right(tf.convert_to_tensor(images))

You are using the right method but in a wrong way :)

nan_to_num is a method of numpy module, not numpy.ndarray. So instead of calling nan_to_num on you data, call it on numpy module giving your data as a paramter:

import numpy as np
data = np.array([1,2,3,np.nan,np.nan,5])
data_without_nan = np.nan_to_num(data)

prints:

array([1., 2., 3., 0., 0., 5.])

In your example:

import numpy as np
val = np.nan_to_num(setTo.ravel())

It would be helpful if you could post the full stack trace, so that we can see which line your error occurs at. In general, the more information you can provide in a question, the better.

In this case, it looks like your full_model_pipeline may somehow become a numpy array. Since you have a one-element pipeline, you could try changing

full_model_pipeline = Pipeline(steps =[
                        ('full_pipeline',full_pipeline),
                        ('model',LinearRegression())
                    ])

full_model_pipeline.fit(X_train,y_train)

to

model = LinearRegression()
model.fit(X_train, y_train)