code: def get_mlp_layer_label_mismatch_penalties(
      non_assignment_penalty, class_or_reg, list_of_activations=None
  ):
      """Gets the label mismatch penalty for an MLP."""
      rectifiers = neural_network.MLP_RECTIFIERS
      sigmoids = neural_network.MLP_SIGMOIDS
      non_linear_activations = rectifiers + sigmoids
      mlp_layer_labels = neural_network.get_mlp_layer_labels(
          class_or_reg, list_of_activations
      )
      num_labels = len(mlp_layer_labels)
      label_penalties = np.zeros((num_labels, num_labels))
      for i in range(num_labels):
          for j in range(i, num_labels):
              labi = mlp_layer_labels[i]
              labj = mlp_layer_labels[j]
              if labi == labj:
                  cost = 0.0
              elif (labi in rectifiers and labj in rectifiers) or (
                  labi in sigmoids and labj in sigmoids
              ):
                  cost = 0.1
              elif (
                  labi in non_linear_activations
                  and labj in non_linear_activations
              ):
                  cost = 0.25
              else:
                  cost = np.inf
              label_penalties[i, j] = cost * non_assignment_penalty
              label_penalties[j, i] = cost * non_assignment_penalty
      return mlp_layer_labels, label_penalties
  
imports: import numpy
  from .None import neural_network
  from ..gp.kernel import ExpSumOfDistsKernel, SumOfExpSumOfDistsKernel
  from ..utils.oper_utils import opt_transport
label_desc: Use image node_id 3 for calling a global function with example usage: get_mlp_layer_label_mismatch_penalties(non_assignment_penalty, class_or_reg, list_of_activations) and returns: mlp_layer_labels,  label_penalties