code: def main(a: T.handle, b: T.handle) -> None:
      # function attr dict
      T.func_attr({"global_symbol": "main", "T.noalias": True})
      # var definition
      threadIdx_x = T.env_thread("threadIdx.x")
      threadIdx_y = T.env_thread("threadIdx.y")
      blockIdx_x = T.env_thread("blockIdx.x")
      blockIdx_y = T.env_thread("blockIdx.y")
      blockIdx_z = T.env_thread("blockIdx.z")
      A = T.match_buffer(a, [14 * 14 * 256 * 256], dtype="float32")
      B = T.match_buffer(b, [14 * 14 * 512 * 256], dtype="float32")
      # body
      T.launch_thread(blockIdx_z, 196)
      B_local = T.decl_buffer([64], "float32", scope="local")
      Apad_shared = T.decl_buffer([512], "float32", scope="shared")
      Apad_shared_local = T.decl_buffer([8], "float32", scope="local")
      T.launch_thread(blockIdx_y, 8)
      T.launch_thread(blockIdx_x, 4)
      T.launch_thread(threadIdx_y, 8)
      T.launch_thread(threadIdx_x, 8)
      for ff_c_init, nn_c_init in T.grid(8, 8):
          B_local[ff_c_init * 8 + nn_c_init] = T.float32(0)
      for rc_outer, ry, rx in T.grid(32, 3, 3):
          for ax3_inner_outer in T.serial(0, 2):
              Apad_shared[
                  T.ramp(
                      threadIdx_y * 64
                      + threadIdx_x * 8
                      + ax3_inner_outer * 4,
                      1,
                      4,
                  )
              ] = T.if_then_else(
                  1 <= blockIdx_z // 14 + ry
                  and blockIdx_z // 14 + ry < 15
                  and 1 <= rx + blockIdx_z % 14
                  and rx + blockIdx_z % 14 < 15,
                  A[
                      T.ramp(
                          ry * 917504
                          + blockIdx_z * 65536
                          + rx * 65536
                          + rc_outer * 2048
                          + threadIdx_y * 256
                          + blockIdx_x * 64
                          + threadIdx_x * 8
                          + ax3_inner_outer * 4
                          - 983040,
                          1,
                          4,
                      )
                  ],
                  T.broadcast(T.float32(0), 4),
                  dtype="float32x4",
              )
          for rc_inner in T.serial(0, 8):
              for ax3 in T.serial(0, 8):
                  Apad_shared_local[ax3] = Apad_shared[
                      rc_inner * 64 + threadIdx_x * 8 + ax3
                  ]
              for ff_c, nn_c in T.grid(8, 8):
                  B_local[ff_c * 8 + nn_c] = (
                      B_local[ff_c * 8 + nn_c] + Apad_shared_local[nn_c]
                  )
      for ff_inner_inner_inner, nn_inner_inner_inner in T.grid(8, 8):
          B[
              blockIdx_z * 131072
              + blockIdx_y * 16384
              + threadIdx_y * 2048
              + ff_inner_inner_inner * 256
              + blockIdx_x * 64
              + threadIdx_x * 8
              + nn_inner_inner_inner
          ] = B_local[ff_inner_inner_inner * 8 + nn_inner_inner_inner]
  
imports: import pytest
  import tvm
  import tvm.testing
  from tvm import meta_schedule
  from tvm import tir
  from tvm.script import tir
  from tvm.target import Target
label_desc: Use image node_id 1 for calling the Conv2dCuda0 obj's underlying member method code with example usage: obj.main(a, b) without return types