code: def __init__(
      self,
      modality_cfg: D2vModalityConfig,
      embed_dim: int,
      local_encoder: nn.Module,
      project_features: nn.Module,
      fixed_positional_encoder: Optional[nn.Module],
      relative_positional_encoder: Optional[nn.Module],
      context_encoder: nn.Module,
      decoder: nn.Module,
      get_alibi_bias: Optional[
          Callable[[int, int, str, str], torch.Tensor]
      ],
  ):
      super().__init__()
  
      self.modality_cfg = modality_cfg
      self.local_encoder = local_encoder
      self.project_features = project_features
      self.fixed_positional_encoder = fixed_positional_encoder
      self.relative_positional_encoder = relative_positional_encoder
      self.context_encoder = context_encoder
  
      self.decoder = decoder
      self.get_alibi_bias = (
          get_alibi_bias if modality_cfg.use_alibi_encoder else None
      )
  
      self.local_grad_mult = self.modality_cfg.local_grad_mult
  
      self.extra_tokens = None
      if modality_cfg.num_extra_tokens > 0:
          self.extra_tokens = nn.Parameter(
              torch.zeros(1, modality_cfg.num_extra_tokens, embed_dim)
          )
          if not modality_cfg.init_extra_token_zero:
              nn.init.normal_(self.extra_tokens)
          elif self.extra_tokens.size(1) > 1:
              nn.init.normal_(self.extra_tokens[:, 1:])
  
      self.alibi_scale = None
      if self.get_alibi_bias is not None:
          self.alibi_scale = nn.Parameter(
              torch.full(
                  (
                      (
                          modality_cfg.prenet_depth
                          + modality_cfg.model_depth
                      )
                      if modality_cfg.learned_alibi_scale_per_layer
                      else 1,
                      1,
                      self.modality_cfg.num_alibi_heads
                      if modality_cfg.learned_alibi_scale_per_head
                      else 1,
                      1,
                      1,
                  ),
                  modality_cfg.alibi_scale,
                  dtype=torch.float,
              ),
              requires_grad=modality_cfg.learned_alibi_scale,
          )
  
      if modality_cfg.learned_alibi and self.get_alibi_bias is not None:
          assert modality_cfg.alibi_max_pos is not None
          alibi_bias = self.get_alibi_bias(
              batch_size=1,
              time_steps=modality_cfg.alibi_max_pos,
              heads=modality_cfg.num_alibi_heads,
              scale=1.0,
              dtype=torch.float,
              device="cpu",
          )
          self.alibi_bias = nn.Parameter(alibi_bias)
          self.get_alibi_bias = partial(
              _learned_alibi_bias, alibi_bias=self.alibi_bias
          )
  
imports: import logging
  import math
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from collections import namedtuple
  from dataclasses import dataclass
  from functools import partial
  from omegaconf import MISSING, II
  from typing import Optional, Callable
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.modules import GradMultiply
  from fairseq.utils import index_put
  from examples.data2vec.data.modality import Modality
  from .modules import D2vDecoderConfig
label_desc: Use image node_id 1 to create a new ModalitySpecificEncoder object from inherited base classes: nn with example: obj = ModalitySpecificEncoder(modality_cfg, embed_dim, local_encoder, project_features, fixed_positional_encoder, relative_positional_encoder, context_encoder, decoder, get_alibi_bias)