code: def ngram(
      input: torch.Tensor,
      context_size: int,
      ngram_padding: torch.Tensor,
  ):
      # input shape: seq_len, batch_size, state_dim + action_dim
      seq_len, batch_size, feature_dim = input.shape
  
      shifted_list = []
      for i in range(context_size):
          offset = i - context_size // 2
          if offset < 0:
              shifted = torch.cat(
                  (
                      ngram_padding.tile((-offset, batch_size, 1)),
                      input.narrow(0, 0, seq_len + offset),
                  ),
                  dim=0,
              )
          elif offset > 0:
              shifted = torch.cat(
                  (
                      input.narrow(0, offset, seq_len - offset),
                      ngram_padding.tile(offset, batch_size, 1),
                  ),
                  dim=0,
              )
          else:
              shifted = input
          shifted_list.append(shifted)
  
      # shape: seq_len, batch_size, feature_dim * context_size
      return torch.cat(shifted_list, dim=-1)
  
imports: import logging
  import math
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core import parameters, types
  from reagent.models import convolutional_network, fully_connected_network
  from reagent.models.base import ModelBase
  from reagent.models.fully_connected_network import ACTIVATION_MAP
label_desc: Use image node_id 2 for calling a global function with example usage: ngram(input, context_size, ngram_padding) and returns: torch