Type current_input_type = mlir::cast<TypeAttr>(input_types[i]).getValue();
      if (current_input_type != target_input_type) {
        input_values[i] =
            rewriter.create<TF::CastOp>(loc, result_type, input_values[i]);
      }
    }
  }

  const SymbolTable& symbol_table_;
  const bool materialize_derived_attrs_;
  const llvm::SmallDenseSet<StringRef, 4> fixed_elt_type_attrs_{"i32_", "i64_",

// CHECK: %[[ADD:.+]] = stablehlo.add %[[TRANSPOSE_1]], %[[ARG_1]] : tensor<1x4x5x5xf32>
// CHECK: return %[[ADD]]

// -----

// Tests that a `reduce_window{max}(add(convolution(%activation, %weight), %bias), %init_value)`
// with the activation tensor of NCHW format is converted to NHWC convolution +
// add + reduce_window (with max) operation. Transpose ops are inserted to

  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,
                /*targets*/ nullptr, /*ntargets*/ 0,

      // workaround.
      SmallVector<int32_t, 4> new_element_shape_values;

      auto int_values = dense_elem_attr.getValues<APInt>();
      for (auto it = int_values.begin(); it != int_values.end(); ++it) {
        auto dim_value = (*it).getSExtValue();
        if (it == int_values.begin() && dim_value == -1) {
          if (!default_to_single_batch_) {
            const char *error_info =

      }
      session->graph->mu.unlock();
      status->status = session->session->Extend(std::move(graph_def));
      if (!status->status.ok()) {
        // Contract is we always delete input_values[i].
        return false;
      }
      // Note: session->session is not modified if Extend() fails, so
      // we only set last_num_graph_nodes if it succeeds.
      session->last_num_graph_nodes = num_nodes;

TF_CAPI_EXPORT extern void TF_DeleteSession(TF_Session*, TF_Status* status);

// Run the graph associated with the session starting with the supplied inputs
// (inputs[0,ninputs-1] with corresponding values in input_values[0,ninputs-1]).
//
// Any NULL and non-NULL value combinations for (`run_options`,
// `run_metadata`) are valid.
//
//    - `run_options` may be NULL, in which case it will be ignored; or

  }

  OpBuilder builder(attr.getContext());

  const auto dense_int_attr = mlir::dyn_cast<DenseIntElementsAttr>(attr);
  const auto int_values = dense_int_attr.getValues<APInt>();
  float float_val = 0.0f;
  if (!int_values.empty()) {
    const APInt apint_val = *int_values.begin();
    if (dense_int_attr.getType().getElementType().isSignedInteger()) {
      // Get the sign-extended value (=>int64) if the type is signed.

  }];

  let arguments = (ins
    Arg<Variadic<TF_Tensor>, [{the input tensor(s)}]>:$inputs,
    Arg<Variadic<TF_Tensor>, [{scalar initial value(s) for the reduction}]>:$init_values,

    I64ArrayAttr:$dimensions_to_reduce,
    SymbolRefAttr:$reducer
  );

  let results = (outs
    Variadic<TF_Tensor>:$outputs
  );