output_type = input_type;
    } else {
      auto shape = llvm::to_vector<4>(input_type.getShape());
      shape[concat_dimension] = shape[concat_dimension] * inputs.size();
      output_type =
          mlir::RankedTensorType::get(shape, input_type.getElementType());
    }
  } else {
    output_type = input_type;
  }

            val color = Attribute.of("color", String::class.java)
            val shape = Attribute.of("shape", String::class.java)
            val matter = Attribute.of("state", String::class.java)

            configurations {
                consumable("roundBlueLiquidElements") {
                    attributes.attribute(shape, "round")
                    attributes.attribute(color, "blue")

// Do not remove or change the type signature.
// See go.dev/issue/67401.

// Notable members of the hall of shame include:
//   - github.com/dgraph-io/ristretto
//   - github.com/outcaste-io/ristretto
//   - github.com/clubpay/ronykit
//go:linkname cputicks

// Notable members of the hall of shame include:
//   - gvisor.dev/gvisor (from assembly)

#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {

struct InferredShape {
  // Shape of the argument tensor.
  PartialTensorShape shape;

  // If the argument is a resource variable, the type and shape of the
  // variable's value.
  DataType handle_type = DT_INVALID;
  PartialTensorShape handle_shape;
};

      }
      SmallVector<int64_t, 4> result_shape = {leading_dim_v};
      ArrayRef<int64_t> shape = element_type.getShape();
      result_shape.append(shape.begin(), shape.end());
      result_type =
          tensorflow::GetTypeFromTFTensorShape(result_shape, element_dtype);
    }

    // Create a 1-D RankedTensorType for result's shape. Number of elements in
    // it is equal to the rank of the result, if known. Otherwise, the number of

    # Checks that each variable's name and shape match.
    self.assertEqual(len(variable_node_defs), len(var_name_and_shapes))
    for var_name, shape in var_name_and_shapes:
      self.assertIn(var_name, variable_node_defs)
      self.assertEqual(
          shape,
          tensor_shape.TensorShape(
              variable_node_defs[var_name].attr['shape'].shape
          ),
      )

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantOps.h"  // from @llvm-project
#include "mlir/Dialect/SCF/IR/SCF.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project
#include "mlir/Dialect/Tensor/IR/Tensor.h"  // from @llvm-project
#include "mlir/InitAllPasses.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project

  let summary =
    "Replicate TensorList init ops for correct shape assignments in shape inference";

  let description = [{
    If we pass same TensorList to a while op as multiple arguments or just use
    the same TensorList at multiple places and assign different
    TensorListSetItem to elements of TensorList, the shape inference is then
    unable to identify the Shape of these args and thus the input TensorList

    if (ShapedType::isDynamic(num_index_dims)) return failure();

    auto updates = op.getUpdates();

    // Broadcast scalar `updates` in into expected shape as following shape:
    // updates.shape == indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
    if (updates_ty.getRank() == 0 &&
        (std::is_same<OpTy, TF::TensorScatterUpdateOp>::value ||
         std::is_same<OpTy, TF::TensorScatterAddOp>::value)) {

    securityContext:
      privileged: true
    volumeMounts:
    - mountPath: /etc/ssl/certs
      name: etc-ssl-certs
      readOnly: true
    - mountPath: /usr/share/ca-certificates
      name: usr-ca-certs
      readOnly: true
    - mountPath: /var/log
      name: varlog
      readOnly: false
    - mountPath: /var/lib/kube-proxy/kubeconfig
      name: kubeconfig