class_docstr: Returns a tensor with the specified shape with random values from a uniform
  distribution. Samples are uniformly distributed over the half-open interval
  ``[low, high)`` (includes low, but excludes high).
  
  .. math::
     p(x) = \frac{1}{high - low}
  
  For a real number :math:`x`.
  
  When ``high == low``, values of ``low`` will be returned. If ``high < low``,
  the results are officially undefined and may eventually raise an error.
  
  Parameters
  ----------
  shape: <K, i32> (Required)
      * Target output tensor shape.
      * ``K`` is the rank of the output tensor.
        ``shape[k] > 0`` for ``k = 0,..., K-1``.
  low: const<T> (Optional)
      * Lower boundary of the output interval (inclusive). Defaults to ``0.0``.
  high: const<T> (Optional)
      * Upper boundary of the output interval (exclusive). Defaults to ``1.0``.
  seed: const<i32> (Optional)
      * Seed to create a reproducible sequence of values across multiple invokes.
  
  Returns
  -------
  <\*, T>
      * A tensor of the given target output shape filled with random values.
  
  Attributes
  ----------
  T: fp16, fp32
  
  See Also
  --------
  random_categorical, random_bernoulli, random_normal
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code: def default_inputs(self):
      return super().default_inputs() + DefaultInputs(
          low=0.0,
          high=1.0,
          seed=-1,
      )
  
imports: from coremltools.converters.mil.mil import get_new_symbol, get_new_variadic_symbol, types
  from coremltools.converters.mil.mil.input_type import DefaultInputs, InputSpec, TensorInputType
  from coremltools.converters.mil.mil.operation import Operation
  from coremltools.converters.mil.mil.ops.defs._op_reqs import register_op
  from coremltools.converters.mil.mil.types.symbolic import any_symbolic
label_desc: Use image node_id 1 for calling the random_uniform obj's underlying member method code with example usage: obj.default_inputs() and returns: unknown