code: def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast(
      placeholder_4: T.handle,
      placeholder_5: T.handle,
      placeholder_6: T.handle,
      T_cast_2: T.handle,
      global_workspace_2_var: T.handle("uint8"),
  ) -> None:
      placeholder_7 = T.match_buffer(
          placeholder_4, [360000], dtype="int16"
      )
      placeholder_8 = T.match_buffer(
          placeholder_5, [4096], dtype="int16"
      )
      placeholder_9 = T.match_buffer(placeholder_6, [64], dtype="int32")
      T_cast_3 = T.match_buffer(T_cast_2, [215], dtype="int16")
      global_workspace_2_buffer_var = T.match_buffer(
          global_workspace_2_var,
          [7920256],
          dtype="uint8",
          strides=[1],
          elem_offset=0,
          align=16,
      )
      # body
      PaddedInput_let = T.Buffer([360000], "int16")
      with T.LetStmt(
          T.address_of(
              global_workspace_2_buffer_var[7200000], dtype="handle"
          ),
          var=PaddedInput_let.data,
      ):
          for i0_i1_fused, i2, i3 in T.grid(75, 75, 64):
              PaddedInput_let[
                  i0_i1_fused * 4800 + i2 * 64 + i3
              ] = placeholder_7[i0_i1_fused * 4800 + i2 * 64 + i3]
          for ax0_ax1_fused_ax2_fused in T.serial(0, 5625):
              Conv2dOutput_let = T.Buffer([64], "int32")
              with T.LetStmt(
                  T.address_of(
                      global_workspace_2_buffer_var[7920000],
                      dtype="handle",
                  ),
                  var=Conv2dOutput_let.data,
              ):
                  for ff in T.serial(0, 64):
                      Conv2dOutput_let[ff] = 0
                      for rc in T.serial(0, 64):
                          Conv2dOutput_let[ff] = Conv2dOutput_let[
                              ff
                          ] + T.cast(
                              PaddedInput_let[
                                  ax0_ax1_fused_ax2_fused * 64 + rc
                              ],
                              "int32",
                          ) * T.cast(
                              placeholder_8[rc * 64 + ff], "int32"
                          )
                  for ax3_inner_1 in T.serial(0, 64):
                      T_cast_3[
                          ax0_ax1_fused_ax2_fused * 64 + ax3_inner_1
                      ] = T.cast(
                          T.cast(
                              T.max(
                                  T.min(
                                      T.q_multiply_shift(
                                          Conv2dOutput_let[ax3_inner_1]
                                          + placeholder_9[ax3_inner_1],
                                          1843106743,
                                          31,
                                          -6,
                                          dtype="int32",
                                      ),
                                      255,
                                  ),
                                  0,
                              ),
                              "uint8",
                          ),
                          "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 4 for calling the ResnetStructurePlanned obj's underlying member method code with example usage: obj.tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast(placeholder_4, placeholder_5, placeholder_6, T_cast_2, global_workspace_2_var) without return types