class_docstr: This module produces sinusoidal positional embeddings of any length.
class_docstr_tok: ["This","module","produces","sinusoidal","positional","embeddings","of","any","length","."]
code: def __init__(
      self,
      num_positions: int,
      embedding_dim: int,
      padding_idx: Optional[int] = None,
  ):
      super().__init__()
      self.offset = 2
      self.embedding_dim = embedding_dim
      self.padding_idx = padding_idx
      self.make_weights(
          num_positions + self.offset, embedding_dim, padding_idx
      )
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_speech_to_text import Speech2TextConfig
label_desc: Use image node_id 1 to create a new Speech2TextSinusoidalPositionalEmbedding object from inherited base classes: nn with example: obj = Speech2TextSinusoidalPositionalEmbedding(num_positions, embedding_dim, padding_idx)