code: def cntk_baseline_conv2d():
      import cntk as C
      import cntk.contrib.crosstalk.crosstalk_cntk as crct
  
      ci = crct.instance
      input_var = C.input_variable(shape=sample_shape)
      input_reshaped = C.reshape(input_var, (1,) + sample_shape)
      conv_out = C.layers.Convolution2D(
          filter_shape, num_filters, init_bias=C.glorot_uniform()
      )(input_reshaped)
      ci.watch(
          conv_out,
          "conv2d",
          var_type=cstk.Conv2DAttr,
          attr=cstk.Conv2DAttr(
              filter_shape=filter_shape, num_filters=num_filters
          ),
      )
      ci.watch(conv_out, "conv2d_out")
      data = {input_var: input_data}
      ci.set_data(data)
      ci.set_workdir(workdir)
      ci.fetch("conv2d", save=True)
      ci.fetch("conv2d_out", save=True)
      ci.reset()
  
imports: import numpy
  from cntk.contrib import crosstalk
  import tempfile
label_desc: Use image node_id 1 for calling a global function with example usage: cntk_baseline_conv2d() without return types