class_docstr: Model which takes context, memories, candidates and encodes them.
class_docstr_tok: ["Model","which","takes","context",",","memories",",","candidates","and","encodes","them","."]
docstr: Encode the context and memories.
docstr_tok: ["Encode","the","context","and","memories","."]
code: def encode_context_memory(
      self, context_w, memories_w, context_segments=None
  ):
      """
      Encode the context and memories.
      """
      # [batch, d]
      if context_w is None:
          # it's possible that only candidates were passed into the
          # forward function, return None here for LHS representation
          return None, None
  
      context_h = self.context_encoder(
          context_w, segments=context_segments
      )
  
      if memories_w is None:
          return [], context_h
  
      bsz = memories_w.size(0)
      memories_w = memories_w.view(-1, memories_w.size(-1))
      memories_h = self.memory_transformer(memories_w)
      memories_h = memories_h.view(bsz, -1, memories_h.size(-1))
  
      context_h = context_h.unsqueeze(1)
      context_h, weights = self.attender(context_h, memories_h)
  
      return weights, context_h
  
file_path: ParlAI/parlai/agents/transformer/modules/mem_net.py
imports: import torch
  import torch.nn
  from typing import Optional, Tuple
  from parlai.agents.transformer.modules import create_embeddings, BasicAttention, TransformerEncoder, TransformerResponseWrapper
  from parlai.core.opt import Opt
  from parlai.core.torch_agent import DictionaryAgent
label_desc: Use image node_id 6 for calling the TransformerMemNetModel obj's underlying member method code with example usage: obj.encode_context_memory(context_w, memories_w, context_segments) and returns: weights,  context_h, None,  None, list,  context_h