code: def build_with_random_weights(
      layout: ModelLayout,
      n_vocab: int,
      device,
      model_H: Hyperparams = None,
      verbose=None,
  ) -> Transformer:
      """
      Instantiate a new Transformer model piece with our specifications
  
      :param layout:
      :param n_vocab:
      :param device:
      :param model_H: This should include both the static and dynamic model_H
      :return: a Transformer model_piece that corresponds to this rank
      """
      is_logging_rank = layout.is_logging_rank
  
      if is_logging_rank:
          print(
              "Constructing Transformer with the following model hparams:"
          )
          print_hparams(model_H, "model_H")
  
      if (
          model_H.d_model % 512 != 0
          and torch.device(device).type == "cuda"
      ):
          logging.warning(
              f"d_model of {model_H.d_model} is not divisible by 512 (8*8*8), this means that "
              f"tensorcores won't be used in attention (CUDA cores will be used instead) and this "
              f"will results in a ~20% slowdown"
          )
  
      mp_comm = create_model_parallel_comm(layout)
  
      # Only pass these params to the Transformer constructor if they're in model_H. That way, the
      # defaults specified in the Transformer constructor apply.
      optional_hparam_keys = [
          "zero_out",
          "init_scale",
          "res_scale",
          "afn",
          "include_pos_embeddings",
          "resid_dropout",
          "attn_dropout",
          "emb_dropout",
          "key_bias",
      ]
      optional_hparams = {
          k: v
          for (k, v) in model_H.items()
          if k in optional_hparam_keys
      }
  
      model = Transformer(
          n_ctx=model_H.get("max_n_ctx") or model_H.n_ctx,
          n_vocab=n_vocab,
          d_model=model_H.d_model,
          n_layer=model_H.n_layer,
          heads=model_H.heads,
          mp_comm=mp_comm,
          m_attn=model_H.m_attn,
          m_mlp=model_H.m_mlp,
          verbose=verbose and is_logging_rank,
          **optional_hparams,
      )
  
      # Convert before moving to CUDA -- don't want to OOM
      maybe_convert_weights(
          model,
          fp16_conv_weights=model_H.fp16_conv_weights,
          fp16_embedding_weights=model_H.fp16_embedding_weights,
      )
  
      model = model.to(device)
  
      return model
  
imports: import logging
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import List, Optional, Dict, TypeVar
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import LayerNorm
  from summarize_from_feedback.model_layout import ModelLayout
  from summarize_from_feedback.models.attention import Attention
  from summarize_from_feedback.models.ops import ACT_FNS, Conv1D
  from summarize_from_feedback.utils import exact_div
  from summarize_from_feedback.utils.dist_utils import create_model_parallel_comm, Comm
label_desc: Use image node_id 3 for calling a global function with example usage: build_with_random_weights(layout, n_vocab, device, model_H, verbose) and returns: model