code: def random_normal(
      *,
      mean: Union[float, tf.Tensor, tf.Variable] = 0.0,
      std: Union[float, tf.Tensor, tf.Variable] = 1.0,
      shape: Optional[Union[ivy.NativeShape, Sequence[int]]] = None,
      dtype: DType,
      seed: Optional[int] = None,
      device: Optional[str] = None,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      _check_valid_scale(std)
      shape = _check_bounds_and_get_shape(mean, std, shape).shape
      mean = tf.cast(mean, dtype)
      std = tf.cast(std, dtype)
      if seed:
          tf.random.set_seed(seed)
      return tf.random.normal(shape, mean, std, dtype=dtype, seed=seed)
  
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 2 for calling a global function with example usage: random_normal() and returns: tf