code: def make_error_input(code, input_data, context):
      code_str = {
          "expected_input": {
              "1": "2D array of formed points",
              "2": "3D array of formed tuples",
              "3": (
                  "1D array of indicators or 2D array of "
                  "formed points"
              ),
              "4": (
                  "2D array of indicators or 3D array "
                  "of formed tuples"
              ),
          },
          "additional_context": {
              "0": "",
              "2": " when using a preprocessor",
              "1": (" after the preprocessor has been " "applied"),
          },
          "possible_preprocessor": {
              "0": "",
              "1": " and/or use a preprocessor",
          },
      }
      code_list = str(code)
      err_args = dict(
          expected_input=code_str["expected_input"][code_list[0]],
          additional_context=code_str["additional_context"][
              code_list[1]
          ],
          possible_preprocessor=code_str["possible_preprocessor"][
              code_list[2]
          ],
          input_data=input_data,
          context=context,
          found_size=input_data.ndim,
      )
      err_msg = (
          "{expected_input} expected"
          "{context}{additional_context}. Found {found_size}D array "
          "instead:\ninput={input_data}. Reshape your data"
          "{possible_preprocessor}.\n"
      )
      raise ValueError(err_msg.format(**err_args))
  
imports: import numpy
  from numpy.linalg import LinAlgError
  from sklearn.datasets import make_spd_matrix
  from sklearn.decomposition import PCA
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_X_y, check_random_state
  from .exceptions import PreprocessorError, NonPSDError
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from scipy.linalg import pinvh, eigh
  import sys
  import time
  import warnings
label_desc: Use image node_id 4 for calling a global function with example usage: make_error_input(code, input_data, context) without return types