code: def make_tensor(shape, dtype: dtypes, noncontiguous) -> Tensor:
      r"""Creates a tensor with the given :attr:`shape`, :attr:`device`, and :attr:`dtype`, and filled with
      values uniformly drawn from ``[low, high)``.
  
      If :attr:`low` or :attr:`high` are specified and are outside the range of the :attr:`dtype`'s representable
      finite values then they are clamped to the lowest or highest representable finite value, respectively.
      If ``None``, then the following table describes the default values for :attr:`low` and :attr:`high`,
      which depend on :attr:`dtype`.
  
      +---------------------------+------------+----------+
      | ``dtype``                 | ``low``    | ``high`` |
      +===========================+============+==========+
      | boolean type              | ``0``      | ``2``    |
      +---------------------------+------------+----------+
      | unsigned integral type    | ``0``      | ``10``   |
      +---------------------------+------------+----------+
      | signed integral types     | ``-9``     | ``10``   |
      +---------------------------+------------+----------+
      | floating types            | ``-9``     | ``9``    |
      +---------------------------+------------+----------+
      | complex types             | ``-9``     | ``9``    |
      +---------------------------+------------+----------+
      """
      contiguous = not noncontiguous  # lol
      if dtype is dtypes.bool:
          return Tensor.randint(
              shape=shape, low=0, high=2, contiguous=contiguous
          ).cast(dtypes.bool)
      elif dtype.is_unsigned():
          return Tensor.randint(
              shape=shape, low=0, high=10, contiguous=contiguous
          ).cast(dtype)
      elif dtype.is_int():
          return Tensor.randint(
              shape=shape, low=-9, high=10, contiguous=contiguous
          ).cast(
              dtype
          )  # signed int
      elif dtype.is_float():
          return Tensor.rand(
              shape=shape,
              low=-9,
              high=9,
              dtype=dtype,
              contiguous=contiguous,
          )
      else:
          raise NotImplementedError(f"{dtype} not implemented")
  
imports: import math, unittest, random, copy
  import numpy
  from tinygrad import Tensor, dtypes, Device
  from tinygrad.lazy import LazyBuffer
  from tinygrad.shape.shapetracker import ShapeTracker
  from tinygrad.shape.view import View
  from tinygrad.helpers import CI
label_desc: Use image node_id 11 for calling a global function with example usage: make_tensor(shape, dtype, noncontiguous) and returns: Tensor, Tensor, Tensor, Tensor