code: def __init__(
      self,
      state_dim: int,
      action_dim: int,
      sizes: List[int],
      activations: List[str],
      last_layer_activation: str,
      context_size: int,
      conv_net_params: rlp.ConvNetParameters,
      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.input_width = state_dim + action_dim
      self.input_height = context_size
      self.num_input_channels = 1
  
      num_conv_layers = len(conv_net_params.conv_height_kernels)
      conv_width_kernels = [self.input_width] + [1] * (
          num_conv_layers - 1
      )
  
      cnn_parameters = convolutional_network.CnnParameters(
          conv_dims=[self.num_input_channels]
          + conv_net_params.conv_dims,
          conv_height_kernels=conv_net_params.conv_height_kernels,
          conv_width_kernels=conv_width_kernels,
          pool_types=conv_net_params.pool_types,
          pool_kernels_strides=conv_net_params.pool_kernel_sizes,
          num_input_channels=self.num_input_channels,
          input_height=self.input_height,
          input_width=self.input_width,
      )
      self.conv_net = convolutional_network.ConvolutionalNetwork(
          cnn_parameters,
          [-1] + sizes + [1],
          activations + [last_layer_activation],
          use_layer_norm=use_layer_norm,
      )
  
      self.ngram_padding = torch.zeros(1, 1, state_dim + action_dim)
  
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 NGramConvolutionalNetwork object from inherited base classes: nn with example: obj = NGramConvolutionalNetwork(state_dim, action_dim, sizes, activations, last_layer_activation, context_size, conv_net_params, use_layer_norm)