code: def _check_for_seq_len_0_and_consistent_head_dim_nested_helper(
      param: torch.Tensor, param_name: str, debug=False
  ) -> bool:
      assert isinstance(
          param, NestedTensor
      ), "param should be a jagged NT"
  
      if param._ragged_idx == 1:
          # num_head_dims is ragged
          if debug:
              log.warning(
                  "Fused kernels do not support ragged num_head_dims, %s has a ragged num_heads.",
                  param_name,
              )
          return False
  
      # This is being called inside sdp with shape [batch, heads, {seq_len}, dim]
      if param._min_seqlen == 0:
          if debug:
              log.warning(
                  "Fused kernels do not support seq_len == 0, %s has a seq len of 0.",
                  param_name,
              )
          return False
  
      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 4 for calling a global function with example usage: _check_for_seq_len_0_and_consistent_head_dim_nested_helper(param, param_name, debug) and returns: True, False, False