class_docstr: d_model, n_ctdx, d_attn, n_head are all standard Transformer hyperparams
  attn_dropout, resid_dropout handle dropout rates for different parameters
  init_scale: scalar which can be used to change all inits
  mp_comm: a comm for sharding
class_docstr_tok: ["d_model",",","n_ctdx",",","d_attn",",","n_head","are","all","standard","Transformer","hyperparams","attn_dropout",",","resid_dropout","handle","dropout","rates","for","different","parameters","init_scale",":","scalar","which","can","be","used","to","change","all","inits","mp_comm",":","a","comm","for","sharding"]
docstr: :param x: The new activations coming in, if this is sampling, then the activations
      will have an input_ctx_len of 1
  :param hidden_state: The past activations or past keys, values
  :return:
  activations: output of attention
  hidden_state: keys and values
docstr_tok: [":","param","x",":","The","new","activations","coming","in",",","if","this","is","sampling",",","then","the","activations","will","have","an","input_ctx_len","of","1",":","param","hidden_state",":","The","past","activations","or","past","keys",",","values",":","return",":","activations",":","output","of","attention","hidden_state",":","keys","and","values"]
code: def forward(
      self, x: torch.Tensor, hidden_state: torch.Tensor = None
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """
      :param x: The new activations coming in, if this is sampling, then the activations
          will have an input_ctx_len of 1
      :param hidden_state: The past activations or past keys, values
      :return:
      activations: output of attention
      hidden_state: keys and values
      """
  
      query = self.q_proj(x)
      new_key = self.k_proj(x)
      new_value = self.v_proj(x)
  
      # returns None if no hidden_state
      past_keys, past_values = self._get_past_keys_values(hidden_state)
  
      if past_keys is not None:
          # key, value are from the current batch being passed through
          # the transformer
          key = torch.cat((past_keys, new_key), dim=-2)
          value = torch.cat((past_values, new_value), dim=-2)
      else:
          key = new_key
          value = new_value
  
      a = self.attn_fn(query, key, value)
  
      a = self.c_proj(a)
  
      if self.mp_comm is not None:
          a = self.mp_comm.all_reduce(a, "attn")
  
      total_ctx_len = key.size(-2)
      if total_ctx_len <= self.n_ctx:
          output_context = torch.stack([new_key, new_value])
      else:
          raise ValueError(
              f"hidden_state_ctx_len > self.n_ctx: {total_ctx_len} > {self.n_ctx}"
          )
  
      return self.resid_dropout(a), output_context
  
file_path: summarize-from-feedback/summarize_from_feedback/models/attention.py
imports: from typing import Tuple
  import numpy
  import torch
  import torch.nn
  from summarize_from_feedback.models.ops import Conv1D
  from summarize_from_feedback.utils import exact_div
  from summarize_from_feedback.utils.dist_utils import Comm
label_desc: Use image node_id 3 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, hidden_state) and returns: self,  output_context