code: def _fit_multi(
      X, num_features=10_000, max_dilations_per_kernel=32, seed=None
  ):
      if seed is not None:
          np.random.seed(seed)
  
      _, n_columns, n_timepoints = X.shape
  
      num_kernels = 84
  
      dilations, num_features_per_dilation = _fit_dilations(
          n_timepoints, num_features, max_dilations_per_kernel
      )
  
      num_features_per_kernel = np.sum(num_features_per_dilation)
  
      quantiles = _quantiles(num_kernels * num_features_per_kernel)
  
      num_dilations = len(dilations)
      num_combinations = num_kernels * num_dilations
  
      max_num_channels = min(n_columns, 9)
      max_exponent = np.log2(max_num_channels + 1)
  
      num_channels_per_combination = (
          2 ** np.random.uniform(0, max_exponent, num_combinations)
      ).astype(np.int32)
  
      channel_indices = np.zeros(
          num_channels_per_combination.sum(), dtype=np.int32
      )
  
      num_channels_start = 0
      for combination_index in range(num_combinations):
          num_channels_this_combination = num_channels_per_combination[
              combination_index
          ]
          num_channels_end = (
              num_channels_start + num_channels_this_combination
          )
          channel_indices[
              num_channels_start:num_channels_end
          ] = np.random.choice(
              n_columns, num_channels_this_combination, replace=False
          )
  
          num_channels_start = num_channels_end
  
      biases = _fit_biases_multi(
          X,
          num_channels_per_combination,
          channel_indices,
          dilations,
          num_features_per_dilation,
          quantiles,
          seed,
      )
  
      return (
          num_channels_per_combination,
          channel_indices,
          dilations,
          num_features_per_dilation,
          biases,
      )
  
imports: import numpy
  from sktime.utils.numba.njit import njit
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 4 for calling a global function with example usage: _fit_multi(X, num_features, max_dilations_per_kernel, seed) and returns: num_channels_per_combination,  channel_indices,  dilations,  num_features_per_dilation,  biases