code: def apply_all_kernels(
      X, weights, lengths, biases, dilations, paddings
  ):
      """Apply one kernel to a data set of time series.
  
      Parameters
      ----------
      X : array, shape = (n_samples, n_timestamps)
          Input data.
  
      weights : array, shape = (n_kernels, max(kernel_sizes))
          Weights of the kernels. Zero padding values are added.
  
      lengths : array, shape = (n_kernels,)
          Length of each kernel.
  
      biases : array, shape = (n_kernels,)
          Bias of each kernel.
  
      dilations : array, shape = (n_kernels,)
          Dilation of each kernel.
  
      paddings : array, shape = (n_kernels,)
          Padding of each kernel.
  
      Returns
      -------
      X_new : array, shape = (n_samples, 2 * n_kernels)
          Extracted features using all the kernels.
  
      """
      n_samples, n_timestamps = X.shape
      n_kernels = lengths.size
      X_new = np.empty((n_samples, 2 * n_kernels))
      for i in prange(n_samples):
          for j in prange(n_kernels):
              X_new[
                  i, (2 * j) : (2 * j + 2)
              ] = apply_one_kernel_one_sample(
                  X[i],
                  n_timestamps,
                  weights[j],
                  lengths[j],
                  biases[j],
                  dilations[j],
                  paddings[j],
              )
      return X_new
  
file_path: pyts/pyts/transformation/rocket.py
imports: from numba import njit, prange
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import check_array, check_is_fitted, check_random_state
  from ..base import UnivariateTransformerMixin
label_desc: Use image node_id 3 for calling a global function with example usage: apply_all_kernels(X, weights, lengths, biases, dilations, paddings) and returns: X_new