code: def dirichlet(
      alpha: Union[paddle.Tensor, float, Sequence[float]],
      /,
      *,
      size: Optional[Union[ivy.NativeShape, Sequence[int]]] = None,
      out: Optional[paddle.Tensor] = None,
      seed: Optional[int] = None,
      dtype: Optional[paddle.dtype] = None,
  ) -> paddle.Tensor:
      size = size if size is not None else len(alpha)
      dtype = dtype if dtype is not None else paddle.float64
      if seed is not None:
          paddle.seed(seed)
      res = paddle.to_tensor(
          paddle.distribution.Dirichlet(concentration=alpha).sample(
              shape=size
          ),
          dtype=dtype,
      )
      return res
  
file_path: ivy/ivy/functional/backends/paddle/experimental/random.py
imports: from typing import Optional, Union, Sequence
  import paddle
  from ivy import with_unsupported_device_and_dtypes
  from ivy.functional.backends.paddle import backend_version
  from ivy.utils.exceptions import IvyNotImplementedException
  from ivy.functional.ivy.random import _check_bounds_and_get_shape
  import ivy
  from paddle.device import core
  from ivy import with_supported_device_and_dtypes
label_desc: Use image node_id 1 for calling a global function with example usage: dirichlet() and returns: res