docstr: A wrapper function to validate the `BetterTransformer` implementation. Implements most relevant checks
  that are present in: https://github.com/pytorch/pytorch/blob/0fc7de398636f4b53e6c3fde38b4e48a5ff5b37d/torch/nn/modules/transformer.py#L457-L475
docstr_tok: ["A","wrapper","function","to","validate","the","`","BetterTransformer","`","implementation",".","Implements","most","relevant","checks","that","are","present","in",":","https",":","\/\/github.com\/pytorch\/pytorch\/blob\/0fc7de398636f4b53e6c3fde38b4e48a5ff5b37d\/torch\/nn\/modules\/transformer.py","#","L457-L475"]
code: def validate_bettertransformer(self):
      r"""
      A wrapper function to validate the `BetterTransformer` implementation. Implements most relevant checks
      that are present in: https://github.com/pytorch/pytorch/blob/0fc7de398636f4b53e6c3fde38b4e48a5ff5b37d/torch/nn/modules/transformer.py#L457-L475
      """
      # Sanity checks
      if self.num_heads is None:
          raise ValueError(
              "Number of heads not set for `BetterTransformer` integration."
          )
  
      if self.embed_dim is None:
          raise ValueError(
              "Embedding dimension not set for `BetterTransformer` integration."
          )
  
      if self.norm2_eps is None or self.norm1_eps is None:
          raise ValueError(
              "`norm2_eps` and `norm1_eps` not set for `BetterTransformer` integration."
          )
  
      # Check positional embedding
      if (
          self.pos_emb_type is not None
          and self.pos_emb_type != "absolute"
      ):
          raise ValueError(
              f"Positional embedding type {self.pos_emb_type} not "
              "supported for `BetterTransformer` integration"
          )
  
      # Check norm1 epsilon and norm2 epsilon equality
      if self.norm1_eps != self.norm2_eps:
          raise ValueError(
              "norm1_eps and norm2_eps must be equal for `BetterTransformer` integration."
          )
  
      # Check activation function
      if self.act_fn in USE_AT_OWN_RISK_ACTIVATION_FUNCTIONS:
          logger.warning(
              f"Overridding {self.act_fn} activation with gelu. Use the transformed model at your own risk, the output logits could be significantly different."
          )
          self.act_fn = "gelu"
      elif self.act_fn not in SUPPORTED_ACTIVATION_FUNCTIONS:
          raise ValueError(
              f"Activation function {self.act_fn} not supported"
              " for `BetterTransformer` integration."
          )
      self.use_gelu = (self.act_fn == "gelu") or (
          self.act_fn == "gelu_new"
      )
  
      # Check num_head is even
      if self.num_heads % 2 == 1:
          raise ValueError(
              f"Number of heads {self.num_heads} is not supported"
              " for `BetterTransformer` integration."
              f" Number of heads must be even."
          )
  
imports: from typing import TYPE_CHECKING
  import torch
  from ...utils import logging, recurse_getattr, recurse_setattr
label_desc: Use image node_id 2 for calling the BetterTransformerBaseLayer obj's underlying member method code with example usage: obj.validate_bettertransformer() without return types