x_signature = [
          None if c not in contracting_dims else x_shape[cidx]
          for cidx, c in enumerate(x_labels)
      ]
      y_signature = [
          None if c not in contracting_dims else y_shape[cidx]
          for cidx, c in enumerate(y_labels)
      ]
    return x_shape, y_shape, bias_shape, x_signature, y_signature

  def _create_einsum_model(
      self,

  auto x_shape = Shape(scope, x);
  auto output_shape = Shape(scope, op.output(0));

  // Reduce away broadcasted leading dims.
  auto reduce_x = internal::BroadcastGradientArgs(scope, x_shape, output_shape);
  auto gx_sum =
      ReduceSum(scope, gx, /*axis=*/reduce_x.r0, ReduceSum::KeepDims(true));
  auto gx_sum_reshape = Reshape(scope, gx_sum, x_shape);

  ArrayRef<int64_t> in_shape = ranked_type.getShape();
  if (in_shape.empty() || in_shape[0] < 0) {
    return context_op->emitOpError()
           << "A map_outside_compilation op's input and output shapes must "
              "have rank at least one and the first dimension must be known.";
  }
  int64_t split_size = in_shape[0] / num_cores_per_replica;
  if (in_shape[0] % num_cores_per_replica != 0) {

  )
  def test_einsum_ptq_model(
      self,
      equation: str,
  ):
    _, y_shape, bias_shape, x_signature, y_signature = (
        self._prepare_sample_einsum_datashapes(equation, use_bias=True)
    )

    model = self._create_einsum_model(
        self._input_saved_model_path,
        equation,
        y_shape,
        x_signature,
        y_signature,
        bias_shape,
    )

    }
    return
  }
```
### `-tf-rewrite-tpu-embedding-ops`

_Rewrites TPU embedding send/recv ops by adding TPU embedding deduplication data_

### `-tf-shape-inference`

_Shape inference on TF dialect and ops implementing InferTypeOpInterface_

Fixed point shape refinement pass that utilizes the shape functions
registered on ops using the InferTypeOpInterface as well as by bridging to