docstr: Parameter
      mask_type
          locally_connected
          locally_connected_learnable
      init_type
          same: use L to init learnable part in mask
          ones: use 1 to init learnable part in mask
          random: use random to init learnable part in mask
docstr_tok: ["Parameter","mask_type","locally_connected","locally_connected_learnable","init_type","same",":","use","L","to","init","learnable","part","in","mask","ones",":","use","1","to","init","learnable","part","in","mask","random",":","use","random","to","init","learnable","part","in","mask"]
code: def _initialize_mask(self):
      """
      Parameter
          mask_type
              locally_connected
              locally_connected_learnable
          init_type
              same: use L to init learnable part in mask
              ones: use 1 to init learnable part in mask
              random: use random to init learnable part in mask
      """
      if "locally_connected" in self.mask_type:
          assert self.neighbour_matrix is not None
          L = self.neighbour_matrix.T
          assert L.shape == (self.in_joints, self.in_joints)
          if "learnable" not in self.mask_type:
              self.mask = tf.constant(L)
          else:
              if self.init_type == "same":
                  initializer = L
              elif self.init_type == "ones":
                  initializer = tf.initializers.ones
              elif self.init_type == "random":
                  initializer = tf.random.uniform
              var_mask = tf.Variable(
                  name="mask",
                  shape=[self.in_joints, self.out_joints]
                  if self.init_type != "same"
                  else None,
                  dtype=tf.float32,
                  initial_value=initializer,
              )
              var_mask = tf.nn.softmax(var_mask, axis=0)
              self.mask = var_mask * tf.constant(
                  L != 0, dtype=tf.float32
              )
  
imports: import numpy
  import tensorflow
  from tensorlayer.layers import Layer, Dropout, Dense, Input, BatchNorm, Reshape, Elementwise
  from tensorlayer.models import Model
  from tensorlayer import logging
  from .common import mask_weight, neighbour_matrix
label_desc: Use image node_id 2 for calling the Base_layer obj's underlying member method code with example usage: obj._initialize_mask() without return types