class_docstr: Add time component to each path.
  
  For a path of shape [B, L, C] this adds a time channel to be placed at the first
  index. The time channel will be of length L and scaled to exist in [0, 1].
class_docstr_tok: ["Add","time","component","to","each","path",".","For","a","path","of","shape","[","B",",","L",",","C","]","this","adds","a","time","channel","to","be","placed","at","the","first","index",".","The","time","channel","will","be","of","length","L","and","scaled","to","exist","in","[","0",",","1","]","."]
code: def _transform(self, X, y=None):
      data = np.swapaxes(X, 1, 2)
      # Batch and length dim
      B, L = data.shape[0], data.shape[1]
  
      # Time scaled to 0, 1
      time_scaled = (
          np.linspace(0, 1, L)
          .reshape(1, L)
          .repeat(B, 0)
          .reshape(B, L, 1)
      )
  
      Xt = np.concatenate((time_scaled, data), 2)
      return np.swapaxes(Xt, 1, 2)
  
imports: import numpy
  from sklearn.pipeline import Pipeline
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 for calling the _AddTime obj's underlying member method code with example usage: obj._transform(X, y) and returns: np