code: def tvmgen_default_fused_nn_max_pool2d_cast(
      placeholder_28: T.handle,
      T_cast_6: T.handle,
      fast_memory_6_var: T.handle("uint8"),
      slow_memory_7_var: T.handle("uint8"),
  ) -> None:
      placeholder_29 = T.match_buffer(
          placeholder_28, [802816], dtype="uint8"
      )
      T_cast_7 = T.match_buffer(T_cast_6, [177], dtype="int16")
      fast_memory_6_buffer_var = T.match_buffer(
          fast_memory_6_var,
          [200704],
          dtype="uint8",
          strides=[1],
          elem_offset=0,
          align=16,
      )
      slow_memory_7_buffer_var = T.match_buffer(
          slow_memory_7_var,
          [1418528],
          dtype="uint8",
          strides=[1],
          elem_offset=0,
          align=16,
      )
      # body
      tensor_2_let = T.Buffer([200704], dtype="uint8")
      with T.LetStmt(
          T.address_of(fast_memory_6_buffer_var[0], dtype="handle"),
          var=tensor_2_let.data,
      ):
          for ax0_ax1_fused_4, ax2_4 in T.grid(56, 56):
              for ax3_init in T.serial(0, 64):
                  tensor_2_let[
                      ax0_ax1_fused_4 * 3584 + ax2_4 * 64 + ax3_init
                  ] = T.uint8(0)
              for rv0_rv1_fused_1, ax3_2 in T.grid(9, 64):
                  tensor_2_let[
                      ax0_ax1_fused_4 * 3584 + ax2_4 * 64 + ax3_2
                  ] = T.max(
                      tensor_2_let[
                          ax0_ax1_fused_4 * 3584 + ax2_4 * 64 + ax3_2
                      ],
                      T.if_then_else(
                          ax0_ax1_fused_4 * 2 + rv0_rv1_fused_1 // 3
                          < 112
                          and ax2_4 * 2 + rv0_rv1_fused_1 % 3 < 112,
                          placeholder_29[
                              ax0_ax1_fused_4 * 14336
                              + rv0_rv1_fused_1 // 3 * 7168
                              + ax2_4 * 128
                              + rv0_rv1_fused_1 % 3 * 64
                              + ax3_2
                          ],
                          T.uint8(0),
                          dtype="uint8",
                      ),
                  )
          for ax0_ax1_fused_5, ax2_5, ax3_3 in T.grid(56, 56, 64):
              T_cast_7[
                  ax0_ax1_fused_5 * 3584 + ax2_5 * 64 + ax3_3
              ] = T.cast(
                  tensor_2_let[
                      ax0_ax1_fused_5 * 3584 + ax2_5 * 64 + ax3_3
                  ],
                  "int16",
              )
  
imports: import sys
  import pytest
  import tvm
  from tvm import PoolInfoProperties, WorkspacePoolInfo
  from tvm.script import tir, ir
  from tvm.target import Target
  from tvm.tir import stmt_functor
  from tvm.tir.usmp import utils
label_desc: Use image node_id 2 for calling the LinearStructurePlanned obj's underlying member method code with example usage: obj.tvmgen_default_fused_nn_max_pool2d_cast(placeholder_28, T_cast_6, fast_memory_6_var, slow_memory_7_var) without return types