code: def generate(self, size, dtype):
      if dtype == np.float32:
          print("random float32")
          return self._random_state.uniform(-1, 1, size).astype(dtype)
      else:
          print(
              "random (u)int min=%d max=%d",
              np.iinfo(dtype).min,
              np.iinfo(dtype).max,
          )
          low = np.iinfo(dtype).min
          high = np.iinfo(dtype).max + 1
          return self._random_state.randint(low, high, size, dtype)
  
imports: from typing import List
  import os
  import struct
  import numpy
  import math
  from enum import IntEnum
  import tensorflow
  from ethosu.vela.register_command_stream_generator import CmdMode
  from ethosu.vela.register_command_stream_generator import cmd0
  from ethosu.vela.register_command_stream_generator import cmd1
  import tvm
  from tvm import relay
  import tvm.relay.backend.contrib.ethosu.op
  from tvm.topi.nn.utils import get_pad_tuple
  from tvm.relay.expr_functor import ExprMutator
  from tvm.relay.op.annotation import compiler_begin, compiler_end
  from tvm.relay.backend.contrib.ethosu import preprocess
  import tvm.relay.testing.tf
  from tvm import WorkspaceMemoryPools, WorkspacePoolInfo, PoolInfoProperties
  from tvm.relay.op.contrib.ethosu import partition_for_ethosu
  from tvm.testing.aot import AOTCompiledTestModel, AOTDataLinkage, AOTTestModel, AOTTestRunner, compile_models, run_and_check
label_desc: Use image node_id 2 for calling the InputGenerator obj's underlying member method code with example usage: obj.generate(size, dtype) and returns: self, self