}
      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

func.func @ZerosLike_unranked(%arg0: tensor<*xi32>) -> tensor<*xi32> {
  // CHECK: %[[ZERO:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
  // CHECK: %[[SHAPE:.*]] = "tf.Shape"(%arg0) : (tensor<*xi32>) -> tensor<?xi64>
  // CHECK: "tf.BroadcastTo"(%[[ZERO]], %[[SHAPE]]) : (tensor<i32>, tensor<?xi64>) -> tensor<*xi32>

  %0 = "tf.ZerosLike"(%arg0) : (tensor<*xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}

_Replicate TensorList init ops for correct shape assignments in shape inference_

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
shape is unidentifiable.

    %1 = "tf.VariableShape"(%input) : (tensor<!tf_type.resource<tensor<?x?x?xf32>>>) -> tensor<3xi32>
    // CHECK: %[[READ:.*]] = "tf.ReadVariableOp"(%arg0)
    // CHECK: %[[SHAPE:.*]] = "tf.Shape"(%[[READ]])
    // CHECK: return %[[SHAPE]]
    tf_device.return %1 : tensor<3xi32>
  }) : () -> tensor<3xi32>
  func.return %0 : tensor<3xi32>
}

// CHECK-LABEL: @decompose_variable_shape_i64

_lower_using_switch_merge = true, _num_original_outputs = 6 : i64, _read_only_resource_inputs = [], body = @map_while_body_180, cond = @map_while_cond_170, device = "", is_stateless = true, output_shapes = [#tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<?>], parallel_iterations = 4 : i64, shape_invariant} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<!tf_type.variant<tensor<*xf32>>>, tensor<*x!tf_type.variant>, tensor<?xf32>) -> (tensor<i32>, tensor<i32>,...

  // Reduce along the broadcasted batch dimensions.
  Output sx = Shape(scope, op.input(0));
  Output sy = Shape(scope, op.input(1));

  Output x_batch_shape = Slice(scope, sx, {0}, Sub(scope, Shape(scope, sx), 2));
  Output y_batch_shape = Slice(scope, sy, {0}, Sub(scope, Shape(scope, sy), 2));

  auto reduce =
      internal::BroadcastGradientArgs(scope, x_batch_shape, y_batch_shape);

// CHECK: %[[SHAPE:.*]] = "tf.Shape"(%[[CAST]]) : (tensor<?x1x1024xi8>) -> tensor<3xi64>
// CHECK: %[[SLICE_1:.*]] = "tf.Slice"(%[[SHAPE]], %[[CONST]], %[[CONST_2]]) : (tensor<3xi64>, tensor<1xi32>, tensor<1xi32>) -> tensor<1xi64>
// CHECK: %[[SLICE_2:.*]] = "tf.Slice"(%[[SHAPE]], %[[CONST_2]], %[[CONST_1]]) : (tensor<3xi64>, tensor<1xi32>, tensor<1xi32>) -> tensor<2xi64>

// setting a shape of [-1, 2] with an existing shape [2, -1] would set
// a final shape of [2, 2] based on shape merging semantics.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.
//   * An invalid shape is being set (e.g., the shape being set
//     is incompatible with the existing shape).
TF_CAPI_EXPORT extern void TF_GraphSetTensorShape(TF_Graph* graph,

  Output shape =
      ops::RandomUniformInt(root.WithOpName("shape"), shape_shape,
                            ops::Const(root.WithOpName("minval"), 1),
                            ops::Const(root.WithOpName("maxval"), 20));
  Output reshape_input =
      ops::Placeholder(root.WithOpName("reshape_input"), DT_FLOAT,
                       ops::Placeholder::Shape(TensorShape({500, 500})));
  Output reshape =

    }


    /**
     * Retrieves the share associated with this SMB resource. In
     * the case of <code>smb://</code>, <code>smb://workgroup/</code>,
     * and <code>smb://server/</code> URLs which do not specify a share,
     * <code>null</code> will be returned.
     *
     * @return The share component or <code>null</code> if there is no share
     */
    public String getShare () {