class_docstr: the full GPT language model, with a context size of block_size
code: def pad_to_full_observation(self, hidden_states):
      batch_size, sequence_length, _ = hidden_states.shape
  
      n_pad = (
          self.transition_dim - sequence_length % self.transition_dim
      ) % self.transition_dim
      padding = torch.zeros(
          batch_size,
          n_pad,
          self.embedding_dim,
          device=hidden_states.device,
      )
  
      # [ batch_size x padded_sequence_length' x embedding_dim ]
      hidden_states_pad = torch.cat([hidden_states, padding], dim=1)
      hidden_states_pad = hidden_states_pad.view(
          -1, self.transition_dim, self.embedding_dim
      )
  
      return hidden_states_pad, n_pad
  
imports: import math
  import os
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import numpy
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import functional
  from ....modeling_utils import PreTrainedModel
  from ....utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_trajectory_transformer import TrajectoryTransformerConfig
label_desc: Use image node_id 4 for calling the TrajectoryTransformerModel obj's underlying member method code with example usage: obj.pad_to_full_observation(hidden_states) and returns: hidden_states_pad,  n_pad