code: def __init__(
      self,
      state_dim: int,
      action_dim: int,
      sizes: List[int],
      activations: List[str],
      last_layer_activation: str,
      context_size: int,
      use_layer_norm: bool = False,
  ) -> None:
      assert (
          context_size % 2 == 1
      ), f"Context size is not odd: {context_size}"
      super().__init__()
      self.context_size = context_size
      self.ngram_padding = torch.zeros(1, 1, state_dim + action_dim)
      self.fc = fully_connected_network.FullyConnectedNetwork(
          [(state_dim + action_dim) * context_size] + sizes + [1],
          activations + [last_layer_activation],
          use_layer_norm=use_layer_norm,
      )
  
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 1 to create a new NGramFullyConnectedNetwork object from inherited base classes: nn with example: obj = NGramFullyConnectedNetwork(state_dim, action_dim, sizes, activations, last_layer_activation, context_size, use_layer_norm)