code: def _read_pretrained_embeddings_file(
      file_uri: str,
      embedding_dim: int,
      vocab: Vocabulary,
      namespace: str = "tokens",
  ) -> torch.FloatTensor:
      """
      Returns and embedding matrix for the given vocabulary using the pretrained embeddings
      contained in the given file. Embeddings for tokens not found in the pretrained embedding file
      are randomly initialized using a normal distribution with mean and standard deviation equal to
      those of the pretrained embeddings.
  
      We support two file formats:
  
          * text format - utf-8 encoded text file with space separated fields: [word] [dim 1] [dim 2] ...
            The text file can eventually be compressed, and even resides in an archive with multiple files.
            If the file resides in an archive with other files, then `embeddings_filename` must
            be a URI "(archive_uri)#file_path_inside_the_archive"
  
          * hdf5 format - hdf5 file containing an embedding matrix in the form of a torch.Tensor.
  
      If the filename ends with '.hdf5' or '.h5' then we load from hdf5, otherwise we assume
      text format.
  
      # Parameters
  
      file_uri : `str`, required.
          It can be:
  
          * a file system path or a URL of an eventually compressed text file or a zip/tar archive
            containing a single file.
  
          * URI of the type `(archive_path_or_url)#file_path_inside_archive` if the text file
            is contained in a multi-file archive.
  
      vocab : `Vocabulary`, required.
          A Vocabulary object.
      namespace : `str`, (optional, default=`"tokens"`)
          The namespace of the vocabulary to find pretrained embeddings for.
      trainable : `bool`, (optional, default=`True`)
          Whether or not the embedding parameters should be optimized.
  
      # Returns
  
      A weight matrix with embeddings initialized from the read file.  The matrix has shape
      `(vocab.get_vocab_size(namespace), embedding_dim)`, where the indices of words appearing in
      the pretrained embedding file are initialized to the pretrained embedding value.
      """
      file_ext = get_file_extension(file_uri)
      if file_ext in [".h5", ".hdf5"]:
          return _read_embeddings_from_hdf5(
              file_uri, embedding_dim, vocab, namespace
          )
  
      return _read_embeddings_from_text_file(
          file_uri, embedding_dim, vocab, namespace
      )
  
imports: import io
  import itertools
  import logging
  import re
  import tarfile
  import warnings
  import zipfile
  from typing import Any, cast, Iterator, NamedTuple, Optional, Sequence, Tuple, BinaryIO
  import numpy
  import torch
  from torch.nn.functional import embedding
  from allennlp.common import Tqdm
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.file_utils import cached_path, get_file_extension, is_url_or_existing_file
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.modules.time_distributed import TimeDistributed
  from allennlp.modules.token_embedders.token_embedder import TokenEmbedder
  from allennlp.nn import util
label_desc: Use image node_id 1 for calling a global function with example usage: _read_pretrained_embeddings_file(file_uri, embedding_dim, vocab, namespace) and returns: _read_embeddings_from_text_file, _read_embeddings_from_hdf5