class_docstr: Represents a sequence padding mask.
class_docstr_tok: ["Represents","a","sequence","padding","mask","."]
docstr: :param seq_lens:
      An array where each element represents the length of a sequence.
      *Shape:* :math:`(N)`, where :math:`N` is the batch size.
  :param batch_seq_len:
      The sequence length of the mask.
docstr_tok: [":","param","seq_lens",":","An","array","where","each","element","represents","the","length","of","a","sequence",".","*","Shape",":","*",":","math",":","`","(","N",")","`",",","where",":","math",":","`","N","`","is","the","batch","size",".",":","param","batch_seq_len",":","The","sequence","length","of","the","mask","."]
code: def __init__(self, seq_lens: Tensor, batch_seq_len: int) -> None:
      """
      :param seq_lens:
          An array where each element represents the length of a sequence.
          *Shape:* :math:`(N)`, where :math:`N` is the batch size.
      :param batch_seq_len:
          The sequence length of the mask.
      """
      self.seq_lens = seq_lens
      self.batch_seq_len = batch_seq_len
  
      self.materialized = None
  
file_path: fairseq2/src/fairseq2/nn/padding.py
imports: from __future__ import annotations
  from typing import Any, Optional, Sequence, Tuple, cast
  import torch
  from torch import Tensor
  from fairseq2.data import Collater, SequenceData
  from fairseq2.typing import Device
label_desc: Use image node_id 1 to create a new PaddingMask object  with example: obj = PaddingMask(seq_lens, batch_seq_len)