code: def build_simple_cnn_text_classifier(
      tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
  ) -> Model[List[Doc], Floats2d]:
      """
      Build a simple CNN text classifier, given a token-to-vector model as inputs.
      If exclusive_classes=True, a softmax non-linearity is applied, so that the
      outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
      is applied instead, so that outputs are in the range [0, 1].
      """
      return build_reduce_text_classifier(
          tok2vec=tok2vec,
          exclusive_classes=exclusive_classes,
          use_reduce_first=False,
          use_reduce_last=False,
          use_reduce_max=False,
          use_reduce_mean=True,
          nO=nO,
      )
  
imports: from functools import partial
  from typing import List, Optional, Tuple, cast
  from thinc.api import Dropout, Gelu, LayerNorm, Linear, Logistic, Maxout, Model, ParametricAttention, ParametricAttention_v2, Relu, Softmax, SparseLinear, SparseLinear_v2, chain, clone, concatenate, list2ragged, reduce_first, reduce_last, reduce_max, reduce_mean, reduce_sum, residual, resizable, softmax_activation, with_cpu
  from thinc.layers.chain import init
  from thinc.layers.resizable import resize_linear_weighted, resize_model
  from thinc.types import ArrayXd, Floats2d
  from ...attrs import ORTH
  from ...errors import Errors
  from ...tokens import Doc
  from ...util import registry
  from ..extract_ngrams import extract_ngrams
  from ..staticvectors import StaticVectors
  from .tok2vec import get_tok2vec_width
label_desc: Use image node_id 1 for calling a global function with example usage: build_simple_cnn_text_classifier(tok2vec, exclusive_classes, nO) and returns: build_reduce_text_classifier