code: def te_conv2d():
      batch = 16
      in_channel = 16
      out_channel = 32
      size = 14
      kernel = 3
  
      A = te.placeholder((batch, in_channel, size, size), name="A")
      W = te.placeholder(
          (in_channel, kernel, kernel, out_channel), name="W"
      )
      Apad = te.compute(
          (batch, in_channel, size + 2, size + 2),
          lambda nn, cc, yy, xx: tvm.tir.if_then_else(
              tvm.tir.all(
                  yy >= 1, yy - 1 < size, xx >= 1, xx - 1 < size
              ),
              A[nn, cc, yy - 1, xx - 1],
              0.0,
          ),
          name="Apad",
      )
      rc = te.reduce_axis((0, in_channel), name="rc")
      ry = te.reduce_axis((0, kernel), name="ry")
      rx = te.reduce_axis((0, kernel), name="rx")
      B = te.compute(
          (batch, out_channel, size, size),
          lambda nn, ff, yy, xx: te.sum(
              Apad[nn, rc, yy + ry, xx + rx] * W[rc, ry, rx, ff],
              axis=[rc, ry, rx],
          ),
          name="B",
      )
      return [A, W, B]
  
imports: import numpy
  import tvm
  import tvm.testing
  from tvm import te, tir, topi, relay
  from tvm.script import tir
  import pytest
label_desc: Use image node_id 12 for calling a global function with example usage: te_conv2d() and returns: list