class_docstr: Module for providing token embeddings from a pretrained model.
class_docstr_tok: ["Module","for","providing","token","embeddings","from","a","pretrained","model","."]
code: def forward(self, embedding: torch.Tensor) -> torch.Tensor:
      embedding_shape = torch.onnx.operators.shape_as_tensor(embedding)
  
      # Since embeddings vector is flattened, verify its shape correctness.
      if embedding_shape[1].item() % self.input_embed_dim != 0:
          raise ValueError(
              f"Input embedding_dim {embedding_shape[1]} is not a"
              + f" multiple of specified embedding_dim {self.input_embed_dim}"
          )
  
      # Unflatten embedding Tensor from (batch_size, seq_len * embedding_size)
      # to (batch_size, seq_len, embedding_size).
      num_tokens = embedding_shape[1] // self.input_embed_dim
      new_embedding_shape = torch.cat(
          (
              torch.tensor([-1], dtype=torch.long),
              num_tokens.view(1),
              torch.tensor([self.input_embed_dim], dtype=torch.long),
          )
      )
      reshaped_embed = torch.onnx.operators.reshape_from_tensor_shape(
          embedding, new_embedding_shape
      )
      return self.proj(reshaped_embed)
  
file_path: pytext/pytext/models/embeddings/contextual_token_embedding.py
imports: from typing import Optional
  import torch
  from pytext.config.field_config import ContextualTokenEmbeddingConfig
  from pytext.models.seq_models.base import PlaceholderIdentity
  from torch.nn import Linear
  from .embedding_base import EmbeddingBase
label_desc: Use image node_id 3 for calling the ContextualTokenEmbedding obj's underlying member method code with example usage: obj.forward(embedding) and returns: self