code: def get_input(
      type, nrows, ncols, dtype, order="C", out_dtype=False, index=None
  ):
      rand_mat = cp.random.rand(nrows, ncols) * 10
      rand_mat = cp.array(rand_mat, dtype=dtype, order=order)
  
      if type == "numpy":
          result = np.array(cp.asnumpy(rand_mat), order=order)
  
      if type == "cupy":
          result = rand_mat
  
      if type == "numba":
          result = nbcuda.as_cuda_array(rand_mat)
  
      if type == "cudf":
          result = cudf.DataFrame(rand_mat, index=index)
  
      if type == "cudf-series":
          result = cudf.Series(rand_mat.reshape(nrows), index=index)
  
      if type == "pandas":
          result = pdDF(cp.asnumpy(rand_mat), index=index)
  
      if type == "pandas-series":
          result = pdSeries(
              cp.asnumpy(rand_mat).reshape(
                  nrows,
              ),
              index=index,
          )
  
      if type == "cuml":
          result = CumlArray(data=rand_mat)
  
      if out_dtype:
          return result, np.array(
              cp.asnumpy(rand_mat).astype(out_dtype), order=order
          )
      else:
          return result, np.array(cp.asnumpy(rand_mat), order=order)
  
imports: from pandas import Series
  from cuml.internals.safe_imports import cpu_only_import_from
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.input_utils import convert_dtype
  from cuml.common import has_cupy
  from cuml.internals.input_utils import input_to_cupy_array
  from cuml.common import input_to_host_array
  from cuml.common import input_to_cuml_array, CumlArray
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 12 for calling a global function with example usage: get_input(type, nrows, ncols, dtype, order, out_dtype, index) and returns: result,  np, result,  np