docstr: Build a new model instance.
code: def build_model(
      cls, cfg: Wav2Vec2ClassificationConfig, task: FairseqTask
  ):
      """Build a new model instance."""
      w2v_encoder = Wav2VecEncoder(cfg, None)
      pooling_layer = get_pooling_layer(
          cfg,
          w2v_encoder.w2v_model.encoder.layers[-1].embedding_dim,
          len(task.target_dictionary),
          len(w2v_encoder.w2v_model.encoder.layers),
      )
      return cls(cfg, w2v_encoder, pooling_layer)
  
imports: import contextlib
  import logging
  from argparse import Namespace
  from dataclasses import dataclass, field
  from typing import Any, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from omegaconf import II, MISSING, open_dict
  from fairseq import checkpoint_utils, tasks, utils
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
  from fairseq.models import BaseFairseqModel, FairseqEncoder, register_model
  from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES, Wav2Vec2Config
  from fairseq.models.wav2vec.wav2vec2_asr import Embedding, Linear, Wav2VecEncoder, Wav2Vec2AsrConfig
  from fairseq.tasks import FairseqTask
label_desc: Use image node_id 3 for calling the Wav2VecClassification obj's underlying member method code with example usage: obj.build_model(cls, cfg, task) and returns: cls