code: def _are_suitable_types_for_torch_lerp(input, end, weight):
      suitable_types = [
          torch.int8,
          torch.int16,
          torch.int32,
          torch.int64,
          torch.float16,
          torch.bfloat16,
          torch.float32,
          torch.float64,
      ]
  
      if not isinstance(input, torch.Tensor) or not isinstance(
          end, torch.Tensor
      ):
          return False
      else:
          if (
              input.dtype not in suitable_types
              or end.dtype not in suitable_types
          ):
              return False
  
      if not isinstance(weight, float) and not isinstance(
          weight, torch.Tensor
      ):
          return False
      else:
          if isinstance(weight, torch.Tensor):
              if weight.dtype not in [
                  torch.float16,
                  torch.bfloat16,
                  torch.float32,
                  torch.float64,
              ]:
                  return False
  
      return True
  
imports: from typing import Optional, Union, Tuple, List, Sequence
  from numbers import Number
  import torch
  import ivy
  from ivy import promote_types_of_inputs
  from ivy.functional.backends.torch.elementwise import _cast_for_unary_op
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ..None import backend_version
label_desc: Use image node_id 22 for calling a global function with example usage: _are_suitable_types_for_torch_lerp(input, end, weight) and returns: True, False, False, False, False