code: def contextualized_features(
      self,
      x,
      padding_mask,
      mask,
      remove_masked,
      clone_batch: int = 1,
      mask_seeds: Optional[torch.Tensor] = None,
      precomputed_mask=None,
  ):
      if padding_mask is not None:
          padding_mask = self.convert_padding_mask(x, padding_mask)
  
      local_features = x
      if mask and clone_batch == 1:
          local_features = local_features.clone()
  
      orig_B, orig_T, _ = x.shape
      pre_mask_B = orig_B
      mask_info = None
  
      x_pos = None
      if self.fixed_positional_encoder is not None:
          x = x + self.fixed_positional_encoder(x, padding_mask)
  
      if mask:
          if clone_batch > 1:
              x = x.repeat_interleave(clone_batch, 0)
              if mask_seeds is not None:
                  clone_hash = [
                      int(hash((mask_seeds.seed, ind)) % 1e10)
                      for ind in range(clone_batch - 1)
                  ]
                  clone_hash = (
                      torch.tensor([0] + clone_hash).long().view(1, -1)
                  )
  
                  id = mask_seeds.ids
                  id = id.repeat_interleave(clone_batch, 0)
                  id = id.view(-1, clone_batch) + clone_hash.to(id)
                  id = id.view(-1)
                  mask_seeds = MaskSeed(
                      seed=mask_seeds.seed,
                      update=mask_seeds.update,
                      ids=id,
                  )
              if padding_mask is not None:
                  padding_mask = padding_mask.repeat_interleave(
                      clone_batch, 0
                  )
  
          x, mask_info = self.compute_mask(
              x,
              padding_mask,
              mask_seed=mask_seeds,
              apply=self.relative_positional_encoder is not None
              or not remove_masked,
              precomputed_mask=precomputed_mask,
          )
  
      if self.relative_positional_encoder is not None:
          x_pos = self.relative_positional_encoder(x)
  
      masked_padding_mask = padding_mask
      if mask and remove_masked:
          x = mask_info.x_unmasked
          if x_pos is not None:
              x = x + gather_unmasked(x_pos, mask_info)
  
          if padding_mask is not None and padding_mask.any():
              masked_padding_mask = gather_unmasked_mask(
                  padding_mask, mask_info
              )
              if not masked_padding_mask.any():
                  masked_padding_mask = None
          else:
              masked_padding_mask = None
  
      elif x_pos is not None:
          x = x + x_pos
  
      alibi_bias = None
      alibi_scale = self.alibi_scale
  
      if self.get_alibi_bias is not None:
          alibi_bias = self.get_alibi_bias(
              batch_size=pre_mask_B,
              time_steps=orig_T,
              heads=self.modality_cfg.num_alibi_heads,
              dtype=torch.float32,
              device=x.device,
          )
  
          if alibi_scale is not None:
              alibi_scale = alibi_scale.clamp_min(0)
              if alibi_scale.size(0) == 1:
                  alibi_bias = alibi_bias * alibi_scale.squeeze(
                      0
                  ).type_as(alibi_bias)
                  alibi_scale = None
  
          if clone_batch > 1:
              alibi_bias = alibi_bias.repeat_interleave(clone_batch, 0)
  
          if mask_info is not None and remove_masked:
              alibi_bias = masked_alibi(alibi_bias, mask_info)
  
      if self.extra_tokens is not None:
          num = self.extra_tokens.size(1)
          x = torch.cat(
              [self.extra_tokens.expand(x.size(0), -1, -1), x], dim=1
          )
          if masked_padding_mask is not None:
              # B x T
              masked_padding_mask = F.pad(masked_padding_mask, (num, 0))
          if alibi_bias is not None:
              # B x H x T x T
              alibi_bias = F.pad(alibi_bias, (num, 0, num, 0))
  
      x = self.context_encoder(
          x,
          masked_padding_mask,
          alibi_bias,
          alibi_scale[: self.modality_cfg.prenet_depth]
          if alibi_scale is not None
          else None,
      )
  
      return {
          "x": x,
          "local_features": local_features,
          "padding_mask": masked_padding_mask,
          "alibi_bias": alibi_bias,
          "alibi_scale": alibi_scale[self.modality_cfg.prenet_depth :]
          if alibi_scale is not None and alibi_scale.size(0) > 1
          else alibi_scale,
          "encoder_mask": mask_info,
      }
  
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 6 for calling the ModalitySpecificEncoder obj's underlying member method code with example usage: obj.contextualized_features(x, padding_mask, mask, remove_masked, clone_batch, mask_seeds, precomputed_mask) and returns: dict