class_docstr: Convert encodings stored in the pickle file to encoding and mask.
  Transform the pad and resize the embedding to match the generator config.
code: def _pad_and_resize(self, arr, ntokens):
      # Function for padding and resizing a numpy array
  
      arr = torch.tensor(arr)
      embed_dim = arr.shape[1]
  
      arr_padded = torch.zeros(
          ntokens, embed_dim, device=arr.device, dtype=torch.float32
      )
  
      # If the input text is larger than num_text_tokens, clip it.
      if arr.shape[0] > ntokens:
          arr = arr[0:ntokens]
  
      mask = torch.LongTensor(ntokens).zero_()
      if len(arr.shape) > 1:
          mask[0 : arr.shape[0]] = 1
  
      if len(arr.shape) > 1:
          arr_padded[0 : arr.shape[0]] = arr
  
      return arr_padded, mask
  
imports: from typing import List, Optional
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the PickleTransform obj's underlying member method code with example usage: obj._pad_and_resize(arr, ntokens) and returns: arr_padded,  mask