docstr: Args:
      hidden_size: embedding dimension of the model
      num_attention_heads: number of attention heads in the model
      epsilon: epsilon value for layer norm (same value used for all norms)
      intermediate_size: Size of MLP projection. If `DEFAULT_INTERMEDIATE_SIZE` is passed
          it is assumed to be `4 * hidden_size`
code: def set_hidden_heads(
      self, hidden_size, num_attention_heads, epsilon, intermediate_size
  ):
      """
      Args:
          hidden_size: embedding dimension of the model
          num_attention_heads: number of attention heads in the model
          epsilon: epsilon value for layer norm (same value used for all norms)
          intermediate_size: Size of MLP projection. If `DEFAULT_INTERMEDIATE_SIZE` is passed
              it is assumed to be `4 * hidden_size`
      """
      self.hidden_size = hidden_size
      if intermediate_size == DEFAULT_INTERMEDIATE_SIZE:
          self.intermediate_size = 4 * hidden_size
      else:
          self.intermediate_size = intermediate_size
      self.num_attention_heads = num_attention_heads
      self.layernorm_epsilon = epsilon
  
imports: from abc import ABC
  import torch
  import deepspeed
  from deepspeed.ops.transformer.inference.config import DeepSpeedInferenceConfig
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 10 for calling the BaseTransformerContainer obj's underlying member method code with example usage: obj.set_hidden_heads(hidden_size, num_attention_heads, epsilon, intermediate_size) without return types