code: def reshape_dict_features(
      dict_feature_id_batch,
      dict_weight_batch,
      dict_seq_len_batch,
      seq_length,
      idx,
  ):
      r"""
      Creates one-sample batch for dict features for sanity check purposes
      It reads and reshapes id, weight and seq_length feature arrays for given
      input index `idx` from the input batch
  
      Args
  
          dict_feature_id_batch: The batch tensor for ids
          dict_weight_matrix: The batch tensor for weights
          dict_seq_len_matrix: The batch tensor for sequence length
          seq_length: The number of tokens per sequence
          idx: The index of sample in the batch
  
      Return
  
          dict_feature_ids: A tensor of dimensions [ bsz x # dict feature embeddings]
          dict_feature_weights: [ bsz x # dict feature embeddings]
          dict_feature_lens: [ bsz * seq_length ]
  
      """
      dict_feature_ids = dict_feature_id_batch[idx].unsqueeze(0)
      dict_feature_weights = dict_weight_batch[idx].unsqueeze(0)
      dict_feature_lens = dict_seq_len_batch[idx].unsqueeze(0)
      return (dict_feature_ids, dict_feature_weights, dict_feature_lens)
  
imports: from collections import defaultdict
  import torch
  from pytext.models.embeddings.dict_embedding import DictEmbedding
  from pytext.models.embeddings.word_embedding import WordEmbedding
  from pytext.models.model import EmbeddingBase, EmbeddingList
label_desc: Use image node_id 4 for calling a global function with example usage: reshape_dict_features(dict_feature_id_batch, dict_weight_batch, dict_seq_len_batch, seq_length, idx) and returns: dict_feature_ids,  dict_feature_weights,  dict_feature_lens