code: def __init__(
      self,
      img_size=224,
      num_frames=8,
      embedding_dim=768,
      n_input_channels=3,
      n_conv_layers=1,
      frame_stride=1,
      frame_kernel_size=3,
      frame_pooling_kernel_size=1,
      frame_pooling_stride=1,
      kernel_size=7,
      stride=2,
      padding=3,
      pooling_kernel_size=3,
      pooling_stride=2,
      pooling_padding=1,
      *args,
      **kwargs,
  ):
      super().__init__()
      img_height, img_width = pair(img_size)
  
      self.tokenizer = Tokenizer(
          n_input_channels=n_input_channels,
          n_output_channels=embedding_dim,
          frame_stride=frame_stride,
          frame_kernel_size=frame_kernel_size,
          frame_pooling_stride=frame_pooling_stride,
          frame_pooling_kernel_size=frame_pooling_kernel_size,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          pooling_kernel_size=pooling_kernel_size,
          pooling_stride=pooling_stride,
          pooling_padding=pooling_padding,
          max_pool=True,
          activation=nn.ReLU,
          n_conv_layers=n_conv_layers,
          conv_bias=False,
      )
  
      self.classifier = TransformerClassifier(
          sequence_length=self.tokenizer.sequence_length(
              n_channels=n_input_channels,
              frames=num_frames,
              height=img_height,
              width=img_width,
          ),
          embedding_dim=embedding_dim,
          seq_pool=True,
          dropout_rate=0.0,
          attention_dropout=0.1,
          stochastic_depth=0.1,
          *args,
          **kwargs,
      )
  
imports: import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
label_desc: Use image node_id 1 to create a new CCT object from inherited base classes: nn with example: obj = CCT(img_size, num_frames, embedding_dim, n_input_channels, n_conv_layers, frame_stride, frame_kernel_size, frame_pooling_kernel_size, frame_pooling_stride, kernel_size, stride, padding, pooling_kernel_size, pooling_stride, pooling_padding)