code: def random_walk_timeseries(
      mean: float = 0.0,
      std: float = 1.0,
      start: Optional[Union[pd.Timestamp, int]] = pd.Timestamp(
          "2000-01-01"
      ),
      end: Optional[Union[pd.Timestamp, int]] = None,
      length: Optional[int] = None,
      freq: Union[str, int] = None,
      column_name: Optional[str] = "random_walk",
      dtype: np.dtype = np.float64,
  ) -> TimeSeries:
      """
      Creates a random walk univariate TimeSeries, where each step is obtained by sampling a gaussian distribution
      with mean `mean` and standard deviation `std`.
  
      Parameters
      ----------
      mean
          The mean of the gaussian distribution that is sampled at each step.
      std
          The standard deviation of the gaussian distribution that is sampled at each step.
      start
          The start of the returned TimeSeries' index. If a pandas Timestamp is passed, the TimeSeries will have a pandas
          DatetimeIndex. If an integer is passed, the TimeSeries will have a pandas RangeIndex index. Works only with
          either `length` or `end`.
      end
          Optionally, the end of the returned index. Works only with either `start` or `length`. If `start` is
          set, `end` must be of same type as `start`. Else, it can be either a pandas Timestamp or an integer.
      length
          Optionally, the length of the returned index. Works only with either `start` or `end`.
      freq
          The time difference between two adjacent entries in the returned index. In case `start` is a timestamp,
          a DateOffset alias is expected; see
          `docs <https://pandas.pydata.org/pandas-docs/stable/user_guide/TimeSeries.html#dateoffset-objects>`_.
          By default, "D" (daily) is used.
          If `start` is an integer, `freq` will be interpreted as the step size in the underlying RangeIndex.
          The freq is optional for generating an integer index (if not specified, 1 is used).
      column_name
          Optionally, the name of the value column for the returned TimeSeries
      dtype
          The desired NumPy dtype (np.float32 or np.float64) for the resulting series
  
      Returns
      -------
      TimeSeries
          A random walk TimeSeries created as indicated above.
      """
  
      index = generate_index(
          start=start, end=end, freq=freq, length=length
      )
      values = np.cumsum(
          np.random.normal(mean, std, size=len(index)), dtype=dtype
      )
  
      return TimeSeries.from_times_and_values(
          index, values, freq=freq, columns=pd.Index([column_name])
      )
  
file_path: darts/darts/utils/timeseries_generation.py
imports: import math
  from typing import List, Optional, Sequence, Tuple, Union
  import holidays
  import numpy
  import pandas
  from darts import TimeSeries
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
label_desc: Use image node_id 6 for calling a global function with example usage: random_walk_timeseries(mean, std, start, end, length, freq, column_name, dtype) and returns: TimeSeries