// Converts Tile op to HLO DBroadcastInDim and DReshape ops.
  //   For shape [S1, S2] and multiples [M1, M2],
  //     MS1 = M1 * S1; MS2 = M2 * S2
  //
  //   %out_dim_size = [S1, M1, S2, M2]
  //   %broadcast_dimensions = [1, 3];
  //   %broadcast = mhlo.d_broadcast_in_dim(%input, %out_dim_size, %braodcast_dimensions);
  //   %shape = [MS1, MS2]

  const auto& subgraph = model_.subgraphs[0];
  auto float_graph = readonly_model_->subgraphs()->Get(0);
  // The original model reshape->custom->custom->squeeze.
  ASSERT_THAT(*float_graph->operators(), SizeIs(4));
  // The resulting model should be:
  // reshape->dequantize->custom->custom->quantize->squeeze.
  ASSERT_THAT(subgraph->operators, SizeIs(6));
  const std::vector<BuiltinOperator> op_codes = {

      %pop_result = "tf.Reshape"(%slice, %elem_size_const)
      "tf.AssignVariableOp"(%size, %new_size)
    ```

    The pass also works across control flow and functional calls.
  }];
}



def UnrollBatchMatMulPass : Pass<"tf-unroll-batch-matmul", "mlir::func::FuncOp"> {
  let summary = "Unroll TF BatchMatMul op into Reshape, Slice, MatMul, Pack ops.";

    // If the `element_shape` is a known constant (which is defined when calling
    // `tensor_list_stack`) and also valid (not scalar), we rewrite this op to a
    // trivial Reshape op (that doesn't actually change the input's shape) and
    // also populate the shape info to the op result. The shape of the
    // tensorlist is inferred from `num_elements` and `element_shape`.

# tensor 't' has shape [3, 2, 3]
# pass '[-1]' to flatten 't'
reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]

# -1 can also be used to infer the shape

# -1 is inferred to be 9:
reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 2:
reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],

    *   Add `tf.contrib.distributions.bijectors.Permute`.
    *   Add `tf.contrib.distributions.bijectors.Gumbel`.
    *   Add `tf.contrib.distributions.bijectors.Reshape`.
    *   Support shape inference (i.e., shapes containing -1) in the Reshape
        bijector.
*   Add `streaming_precision_recall_at_equal_thresholds,` a method for computing
    streaming precision and recall with `O(num_thresholds + size of