const char tensor1[] = {5, 7};
  const int64_t dims1[] = {1, 2};
  const size_t ndims1 = TF_ARRAYSIZE(dims1);

  const char tensor2[] = {2, 4, 6, 8};
  const int64_t dims2[] = {2, 2};
  const size_t ndims2 = TF_ARRAYSIZE(dims2);

  auto desc = init("list(tensor)");
  TF_Tensor* tmp[] = {
      Int8Tensor(dims1, ndims1, tensor1),
      Int8Tensor(dims2, ndims2, tensor2),
  };

  %5 = stablehlo.dynamic_broadcast_in_dim %1, %4, dims = [3] : (tensor<16xf32>, tensor<4xindex>) -> tensor<?x28x28x16xf32>
  %6 = stablehlo.add %3, %5 : tensor<?x28x28x16xf32>
  %7 = shape.shape_of %6 : tensor<?x28x28x16xf32> -> tensor<4xindex>
  %8 = stablehlo.dynamic_broadcast_in_dim %2, %7, dims = [] : (tensor<f32>, tensor<4xindex>) -> tensor<?x28x28x16xf32>

#else
  const char* device_name_ = tensorflow::DEVICE_CPU;
#endif
};

// Validates that the tensor has shape and type corresponding to
// dims and dtype.
void validate_tensor(TF_Tensor* tensor, int64_t* dims, int64_t num_dims,
                     TF_DataType dtype);

// Copies data of length tensor_size_bytes from values to tensor.
template <typename T>

// Returns the shape of the Tensor referenced by `output` in `graph`
// into `dims`. `dims` must be an array large enough to hold `num_dims`
// entries (e.g., the return value of TF_GraphGetTensorNumDims).
//
// If the number of dimensions in the shape is unknown or the shape is
// a scalar, `dims` will remain untouched. Otherwise, each element of
// `dims` will be set corresponding to the size of the dimension. An

      %2 = stablehlo.reshape %1 : (tensor<i32>) -> tensor<1xi32>
      %3 = stablehlo.concatenate %2, %cst_0, dim = 0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>
      %4 = stablehlo.dynamic_broadcast_in_dim %arg2, %3, dims = [1] : (tensor<3xf32>, tensor<2xi32>) -> tensor<?x3xf32>
      %5 = stablehlo.add %0, %4 : tensor<?x3xf32>
      return %5 : tensor<?x3xf32>
    }
}

  TFE_DeleteContextOptions(opts);

  std::vector<int64_t> dims(4, 1);
  TFE_Op* op = TFE_NewOp(ctx, "AvgPool", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TF_Tensor* tensor =
      TF_AllocateTensor(TF_FLOAT, dims.data(), dims.size(), sizeof(float));
  float tensor_data[] = {1};

  auto gx = SelectV2(scope, c, grad_inputs[0], zeros);
  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));

- Scheduler framework: PreBind implementations are now allowed to return Pending and Unschedulable status...

* Increase request timeout based on termination grace period ([#31275](https://github.com/kubernetes/kubernetes/pull/31275), [@dims](https://github.com/dims))
* Skip safe to detach check if node API object no longer exists ([#30737](https://github.com/kubernetes/kubernetes/pull/30737), [@saad-ali](https://github.com/saad-ali))

    // Replace the original strided_slice.
    uint64_t revised_begin_mask = strided_slice_op.getBeginMask();
    uint64_t revised_end_mask = strided_slice_op.getEndMask();
    // Since we expand the dims, we need to apply them to the begin_mask &
    // end_mask.
    revised_begin_mask |= strided_slice_op.getNewAxisMask();
    revised_end_mask |= strided_slice_op.getNewAxisMask();

    // Enforce operator precedence.