code: def as_dtype(type_value):
      if isinstance(type_value, DType):
          return type_value
      if ivy.is_native_dtype(type_value):
          return DType(
              tf_frontend.tensorflow_type_to_enum[
                  ivy.as_ivy_dtype(type_value)
              ]
          )
      if type_value in tf_frontend.tensorflow_enum_to_type:
          return DType(type_value)
      if type_value in tf_frontend.tensorflow_type_to_enum:
          return DType(tf_frontend.tensorflow_type_to_enum[type_value])
      if type_value is float:
          return DType(1)
      if type_value is bool:
          return DType(10)
      if isinstance(type_value, np_frontend.dtype):
          return DType(
              tf_frontend.tensorflow_type_to_enum[type_value.ivy_dtype]
          )
      if issubclass(type_value, np_frontend.generic):
          return DType(
              tf_frontend.tensorflow_type_to_enum[
                  np_frontend.numpy_scalar_to_dtype[type_value]
              ]
          )
      raise ivy.utils.exceptions.IvyException(
          f"Cannot convert the argument 'type_value': {type_value!r} "
          "to a TensorFlow Dtype"
      )
  
imports: import ivy
  import ivy.functional.frontends.tensorflow
  import ivy.functional.frontends.numpy
  from ivy.functional.frontends.tensorflow.func_wrapper import to_ivy_arrays_and_back, handle_tf_dtype
label_desc: Use image node_id 1 for calling a global function with example usage: as_dtype(type_value) and returns: type_value, DType, DType, DType, DType, DType, DType, DType