code: def randint(
      low: Union[float, tf.Tensor, tf.Variable],
      high: 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]:
      if not dtype:
          dtype = ivy.default_int_dtype()
      dtype = ivy.as_native_dtype(dtype)
      _randint_check_dtype_and_bound(low, high, dtype)
      shape = _check_bounds_and_get_shape(low, high, shape).shape
      low = tf.cast(low, "float32")
      high = tf.cast(high, "float32")
      if seed:
          tf.random.set_seed(seed)
      return tf.cast(
          tf.random.uniform(shape, low, high, "float32", seed=seed),
          dtype,
      )
  
file_path: ivy/ivy/functional/backends/tensorflow/random.py
imports: from typing import Optional, Union, Sequence
  import tensorflow
  from tensorflow.python.framework.dtypes import DType
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ivy.functional.ivy.random import _check_bounds_and_get_shape, _randint_check_dtype_and_bound, _check_valid_scale
  from .None import backend_version
label_desc: Use image node_id 4 for calling a global function with example usage: randint() and returns: tf