class_docstr: Vision Transformer Model.
class_docstr_tok: ["Vision","Transformer","Model","."]
code: def forward(self, input):
      if self.pre_process:
          rearranged_input = einops.rearrange(
              input,
              "b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
              p1=self.patch_dim,
              p2=self.patch_dim,
          )
  
          # [b num_patch patch_dim*patch_dim*c] ->  [b, s, h]; s:=num_patch, h:=hidden
          encoder_output = self.linear_encoder(rearranged_input)
  
          concatenated_tokens = encoder_output
          if self.class_token:
              cls_tokens = self.cls_token.expand(
                  encoder_output.shape[0], -1, -1
              )
              concatenated_tokens = torch.cat(
                  (cls_tokens, encoder_output), dim=1
              )
  
          if self.position_embedding_type == "learned_absolute":
              token_embeddings = (
                  concatenated_tokens
                  + self.position_embeddings(
                      self.position_ids[
                          :, : concatenated_tokens.shape[1]
                      ]
                  )
              )
          elif self.position_embedding_type == "learned_parameters":
              token_embeddings = (
                  concatenated_tokens + self.position_embeddings
              )
          else:
              raise ValueError(
                  f"Unrecognized position embedding type: {self.position_embedding_type}."
              )
  
          # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
          token_embeddings = self.drop_patch(token_embeddings)
  
          if self.preprocess_layernorm is not None:
              token_embeddings = self.preprocess_layernorm(
                  token_embeddings
              )
  
          # [b s h] => [s b h]
          token_embeddings = token_embeddings.transpose(
              0, 1
          ).contiguous()
          hidden_states = self.embedding_dropout(token_embeddings)
      else:
          hidden_states = input
  
      # 0 represents masking, 1 represents not masking
      # attention_mask = torch.zeros(
      #     [1, 1, hidden_states.shape[0], hidden_states.shape[0]],
      #     device=hidden_states.device,
      #     dtype=torch.bool,
      # )
      hidden_states = self.transformer(hidden_states, None)
  
      if self.post_process:
          # [s b h] => [b s h]
          if self.single_token_output:
              hidden_states = hidden_states[0]
          else:
              hidden_states = hidden_states.transpose(0, 1).contiguous()
  
      return hidden_states
  
imports: import math
  from functools import partial
  import einops
  import torch
  import torch.nn.functional
  from nemo.collections.nlp.modules.common.megatron.fused_layer_norm import get_layer_norm
  from nemo.collections.nlp.modules.common.megatron.module import MegatronModule
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults, init_method_normal, scaled_init_method_normal
  from nemo.collections.vision.modules.common.megatron.vision_transformer import ParallelVisionTransformer
label_desc: Use image node_id 3 for calling the VitBackbone obj's underlying member method code with example usage: obj.forward(input) and returns: hidden_states