class_docstr: PositionEmbedding represents the positional information of tokens
  consisting of two optional types: constant(untrainable) and trainable.
class_docstr_tok: ["PositionEmbedding","represents","the","positional","information","of","tokens","consisting","of","two","optional","types",":","constant","(","untrainable",")","and","trainable","."]
code: def __init__(self, max_len, embed_dim, use_const, name, **kwargs):
      super().__init__(**kwargs)
      self.max_len = max_len
      self.embed_dim = embed_dim
      self.use_const = use_const
      self.pos_name = name
      self.pos_embed = self.get_pos_embedding_matrix(
          self.max_len, self.embed_dim, self.use_const, self.pos_name
      )
  
file_path: delta/delta/layers/sub_tf.py
imports: from absl import logging
  import delta.compat
  import numpy
  from delta.layers.base_layer import Layer
  from delta.layers.utils_tf import shape_list
label_desc: Use image node_id 1 to create a new PositionEmbedding object from inherited base classes: Layer with example: obj = PositionEmbedding(max_len, embed_dim, use_const, name)