code: def falcon_build_alibi_tensor_patched(
      attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype
  ) -> torch.Tensor:
      batch_size, seq_length = attention_mask.shape
      closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
      base = torch.tensor(
          2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))),
          device=attention_mask.device,
          dtype=torch.float32,
      )
      powers = torch.arange(
          1,
          1 + closest_power_of_2,
          device=attention_mask.device,
          dtype=torch.int32,
      )
      slopes = torch.pow(base, powers)
  
      if closest_power_of_2 != num_heads:
          extra_base = torch.tensor(
              2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))),
              device=attention_mask.device,
              dtype=torch.float32,
          )
          num_remaining_heads = min(
              closest_power_of_2, num_heads - closest_power_of_2
          )
          extra_powers = torch.arange(
              1,
              1 + 2 * num_remaining_heads,
              2,
              device=attention_mask.device,
              dtype=torch.int32,
          )
          slopes = torch.cat(
              [slopes, torch.pow(extra_base, extra_powers)], dim=0
          )
  
      # Note: alibi will added to the attention bias that will be applied to the query, key product of attention
      # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
      # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
      # => the query_length dimension will then be broadcasted correctly
      # This is more or less identical to T5's relative position bias:
      # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
      # NOTE: remove the .bfloat16() cast here as PyTorch ONNX export rather casts to complex128 if this is used, resulting in a onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph error.
      arange_tensor = (
          (attention_mask.cumsum(dim=-1) - 1) * attention_mask
      )[:, None, :]
      alibi = slopes[..., None] * arange_tensor
      return alibi.reshape(batch_size * num_heads, 1, seq_length).to(
          dtype
      )
  
imports: import dataclasses
  import functools
  import inspect
  import math
  import sys
  import types
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
  import transformers
  from packaging import version
  from transformers.models.speecht5.modeling_speecht5 import SpeechT5EncoderWithSpeechPrenet
  from transformers.utils import is_torch_available
  from ...configuration_utils import _transformers_version
  from ...utils import logging
label_desc: Use image node_id 6 for calling a global function with example usage: falcon_build_alibi_tensor_patched(attention_mask, num_heads, dtype) and returns: alibi