code: def _is_safe_to_get_storage_as_tensor(tensor: torch.Tensor):
      # This function checks if a nested tensor is valid for
      # use with the flash-attention and efficient_attention kernels without
      # needing to call contiguous on the nested tensor input.
      # It checks that the storage offsets' adjacent_differences are a constant
      # mutiple of the previous tensor in the nested tensor and that the strides
      # are monitonically decreasing. This check is done after calling transpose on
      # the nested tensor resulting in a Nt of shape [bsz, {seq_len}, num_heads, dim]
  
      # Returns a boolean indicating if contiguous needs to be called for input
      assert isinstance(tensor, NestedTensor)
      offsets = tensor.offsets()
      strides = tensor._stride
  
      n_tensors = offsets.size(0) - 1
      if n_tensors <= 1:
          return True
  
      # Check initially that the tensor strides are in strictly descending order
      prev_stride = strides[1]
      for stride in strides[2:]:
          if prev_stride <= stride:
              # This would mean that the last stride is greater than the seq_len
              # stride
              return False
          prev_stride = stride
  
      # Congrats you made it!
      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 12 for calling a global function with example usage: _is_safe_to_get_storage_as_tensor(tensor) and returns: True, True, False