class_docstr: Temporal Transformer
  
  Args:
      - width (`int`):
          Dimension used in Residual Attention.
      - layers (`int`):
          Number of layers.
      - heads (`int`):
          Number of heads.
      - attn_mask (`torch.Tensor`):
          Attention mask.
class_docstr_tok: ["Temporal","Transformer","Args",":","-","width","(","`","int","`",")",":","Dimension","used","in","Residual","Attention",".","-","layers","(","`","int","`",")",":","Number","of","layers",".","-","heads","(","`","int","`",")",":","Number","of","heads",".","-","attn_mask","(","`","torch.Tensor","`",")",":","Attention","mask","."]
code: def forward(self, x: torch.Tensor):
      return self.resblocks(x)
  
imports: import torch
  from torch import nn
  from collections import OrderedDict
  from towhee.models.utils.weight_init import trunc_normal_
  from towhee.models.utils.init_vit_weights import init_vit_weights
label_desc: Use image node_id 2 for calling the TemporalTransformer obj's underlying member method code with example usage: obj.forward(x) and returns: self