code: def generate_sm75_tensor_op_1688(
      out_dtype,
      arg0_dtype,
      arg1_dtype,
      op_creator,
      check_align,
      _,
      profile_all_alignments=False,
      accumlator_dtype="float32",
  ):
      """Generate GEMM or Conv2D kernels for Turing."""
      assert out_dtype in ["float32", "float16", "int32"]
      min_cc = 75
      max_cc = 1024
  
      if arg0_dtype == "float16" and arg1_dtype == "float16":
          math_instructions = [
              MathInstruction(
                  [16, 8, 8],
                  DataType.f16,
                  DataType.f16,
                  dtype_map[out_dtype],
                  dtype_map[accumlator_dtype],
                  OpcodeClass.TensorOp,
                  MathOperation.multiply_add,
              )
          ]
          alignment_constraints = [8, 4, 2, 1]
          tile_descriptions = [
              ([256, 128, 32], 2, [4, 2, 1], min_cc, max_cc),
              ([128, 256, 32], 2, [2, 4, 1], min_cc, max_cc),
              ([128, 128, 32], 2, [2, 2, 1], min_cc, max_cc),
              ([64, 128, 32], 2, [2, 2, 1], min_cc, max_cc),
              ([128, 64, 32], 2, [2, 2, 1], min_cc, max_cc),
              ([64, 64, 32], 2, [2, 2, 1], min_cc, max_cc),
              ([64, 128, 64], 2, [1, 2, 2], min_cc, max_cc),
          ]
  
      elif "int8" in arg0_dtype and "int8" in arg1_dtype:
          assert out_dtype == "int32"
          math_instructions = [
              MathInstruction(
                  [8, 8, 16],
                  dtype_map[arg0_dtype],
                  dtype_map[arg1_dtype],
                  DataType.s32,
                  DataType.s32,
                  OpcodeClass.TensorOp,
                  MathOperation.multiply_add_saturate,
              )
          ]
          alignment_constraints = [16, 8, 4, 2, 1]
          tile_descriptions = [
              ([256, 128, 64], 2, [4, 2, 1], min_cc, max_cc),
              ([128, 256, 64], 2, [2, 4, 1], min_cc, max_cc),
              ([128, 128, 64], 2, [2, 2, 1], min_cc, max_cc),
              ([64, 256, 64], 2, [1, 4, 1], min_cc, max_cc),
              ([256, 64, 64], 2, [4, 1, 1], min_cc, max_cc),
              ([64, 128, 64], 2, [2, 2, 1], min_cc, max_cc),
              ([128, 64, 64], 2, [2, 2, 1], min_cc, max_cc),
              ([64, 64, 64], 2, [2, 2, 1], min_cc, max_cc),
          ]
      elif (
          arg0_dtype == "float32"
          and arg1_dtype == "float32"
          and out_dtype == "float32"
      ):
          return generate_sm50_simt(
              out_dtype,
              arg0_dtype,
              arg1_dtype,
              op_creator,
              accumlator_dtype,
          )
      else:
          raise NotImplementedError()
  
      alignment_constraints = [
          align for align in alignment_constraints if check_align(align)
      ]
      assert len(alignment_constraints) > 0
  
      if not profile_all_alignments:
          alignment_constraints = [alignment_constraints[0]]
  
      def get_tile_descriptions(math_inst):
          return [
              TileDescription(
                  threadblock_shape,
                  stages,
                  warp_count,
                  math_inst,
                  min_cc,
                  max_cc,
              )
              for threadblock_shape, stages, warp_count, min_cc, max_cc in tile_descriptions
          ]
  
      return generate_tensor_op_common(
          math_instructions,
          alignment_constraints,
          get_tile_descriptions,
          op_creator,
      )
  
imports: import logging
  import os
  import re
  import tempfile
  import subprocess
  import multiprocessing
  import tvm._ffi
  from tvm.tir import IntImm
  from tvm.runtime import Object
  from .None import _ffi_api
  from .library import MathInstruction, DataType, DataTypeTag, OpcodeClass, MathOperation, TileDescription, EpilogueFunctor
  from .gemm_operation import instantiate_gemm_template
  from .conv2d_operation import instantiate_conv2d_template
label_desc: Use image node_id 3 for calling a global function with example usage: generate_sm75_tensor_op_1688(out_dtype, arg0_dtype, arg1_dtype, op_creator, check_align, _, profile_all_alignments, accumlator_dtype) and returns: generate_tensor_op_common, unknown, generate_sm50_simt