code: def gamma(
      alpha: Union[float, tf.Tensor, tf.Variable],
      beta: Union[float, tf.Tensor, tf.Variable],
      /,
      *,
      shape: Optional[Union[ivy.NativeShape, Sequence[int]]] = None,
      device: Optional[str] = None,
      dtype: Optional[Union[DType, ivy.Dtype]] = None,
      seed: Optional[int] = None,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      pass
  
file_path: ivy/ivy/functional/backends/tensorflow/experimental/random.py
imports: from typing import Union, Optional, Sequence
  import tensorflow
  from tensorflow.python.framework.dtypes import DType
  import ivy
  from ..None import backend_version
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.ivy.random import _check_shapes_broadcastable
label_desc: Use image node_id 3 for calling a global function with example usage: gamma() without return types