code: def uniform(low=0, high=1, shape=_Null, dtype=_Null, **kwargs):
      """Draw random samples from a uniform distribution.
  
      Samples are uniformly distributed over the half-open interval *[low, high)*
      (includes *low*, but excludes *high*).
  
      Parameters
      ----------
      low : float or Symbol, optional
          Lower boundary of the output interval. All values generated will be
          greater than or equal to low. The default value is 0.
      high : float or Symbol, optional
          Upper boundary of the output interval. All values generated will be
          less than high. The default value is 1.0.
      shape : int or tuple of ints, optional
          The number of samples to draw. If shape is, e.g., `(m, n)` and `low` and
          `high` are scalars, output shape will be `(m, n)`. If `low` and `high`
          are Symbols with shape, e.g., `(x, y)`, then output will have shape
          `(x, y, m, n)`, where `m*n` samples are drawn for each `[low, high)` pair.
      dtype : {'float16', 'float32', 'float64'}, optional
          Data type of output samples. Default is 'float32'
  
      Returns
      -------
      Symbol
          If input `shape` has dimensions, e.g., `(m, n)`, and `low` and `high` are
          scalars, returned Symbol will resolve to shape `(m, n)`. If `low` and `high`
          are Symbols with shape, e.g., `(x, y)`, returned Symbol will have shape
          `(x, y, m, n)`, where `m*n` samples are drawn for each `[low, high)` pair.
      """
      return _random_helper(
          _internal._random_uniform,
          _internal._sample_uniform,
          [low, high],
          shape,
          dtype,
          kwargs,
      )
  
file_path: mxnet/python/mxnet/symbol/random.py
imports: from ..base import numeric_types, _Null
  from .None import _internal
  from .symbol import Symbol
label_desc: Use image node_id 2 for calling a global function with example usage: uniform(low, high, shape, dtype) and returns: _random_helper