code: def _check_for_seq_len_0_nested(
      params: SDPAParams, debug=False
  ) -> bool:
      # When this function is called we are assured that the nt is dim==4
      q_is_safe = (
          _check_for_seq_len_0_and_consistent_head_dim_nested_helper(
              params.query, "query", debug
          )
          if params.query.is_nested
          else True
      )
      # short circuit if any is unsafe
      if not q_is_safe:
          return False
  
      k_is_safe = (
          _check_for_seq_len_0_and_consistent_head_dim_nested_helper(
              params.key, "key", debug
          )
          if params.key.is_nested
          else True
      )
      # short circuit if any is unsafe
      if not k_is_safe:
          return False
  
      v_is_safe = (
          _check_for_seq_len_0_and_consistent_head_dim_nested_helper(
              params.value, "value", debug
          )
          if params.value.is_nested
          else True
      )
      # short circuit if any is unsafe
      if not v_is_safe:
          return False
  
      # We now know none of the inputs have ragged num_heads, so we can safely
      # access .size(1)
      q_num_heads = params.query.size(1)
      k_num_heads = params.key.size(1)
      v_num_heads = params.value.size(1)
      same_num_heads = (
          q_num_heads == k_num_heads and q_num_heads == v_num_heads
      )
  
      if not same_num_heads:
          if (
              params.query.requires_grad
              or params.key.requires_grad
              or params.value.requires_grad
          ):
              if debug:
                  log.warning(
                      "Both fused kernels do not support training with broadcasted NT inputs."
                  )
              return False
          return _try_broadcast_param_size(
              q_num_heads, k_num_heads, v_num_heads, "num heads", debug
          )
      return True
  
imports: import logging
  import math
  from typing import Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.backends.cuda import can_use_efficient_attention, can_use_flash_attention, flash_sdp_enabled, math_sdp_enabled, mem_efficient_sdp_enabled, SDPAParams, SDPBackend
  from .nested_tensor import buffer_from_jagged, NestedTensor, ViewNestedFromBuffer
label_desc: Use image node_id 6 for calling a global function with example usage: _check_for_seq_len_0_nested(params, debug) and returns: True, False, False, False, _try_broadcast_param_size, False