//   [H, W, ..., filter_in_depth, G, out_depth / G].
      auto new_shape = llvm::to_vector<6>(filter_shape);
      new_shape.back() = feature_group_count;
      new_shape.push_back(filter_shape.back() / feature_group_count);
      Type filter_element_ty = filter_ty.getElementType();
      auto ty =
          tensorflow::GetTypeFromTFTensorShape(new_shape, filter_element_ty);

      SmallVector<int64_t> new_shape(shape.begin(), shape.end());
      // If dimension of the input type is dynamic. Update the
      // bounds of the dim with the new type if needed.
      for (int i = 0; i < input_ty.getShape().size(); i++) {
        if (hlo::isDynamicDimSize(input_ty.getShape()[i])) {
          new_bounds[i] = new_shape[i];
          new_shape[i] = ShapedType::kDynamic;
        }
      }

  Type dtype = getElementTypeOrSelf(type);
  if (RankedTensorType ty = type.dyn_cast<RankedTensorType>()) {
    llvm::SmallVector<int64_t, 4> shape = {dim};
    shape.append(ty.getShape().begin(), ty.getShape().end());
    return tensorflow::GetTypeFromTFTensorShape(shape, dtype);
  }
  return type;
}

Type GetTensorTypeForTensorList(Type element_type, TF::VariantType handle_dtype,

        }

        shape_inference::ShapeHandle handle;
        TF_RETURN_IF_ERROR(
            context->MakeShapeFromPartialTensorShape(arg_shape.shape, &handle));
        TF_RETURN_IF_ERROR(shape_refiner->SetShape(n, 0, handle));
      }
    }

    // Sometimes we have VariableShape nodes in while loop (after Enter nodes).
    // They won't be constant-folded because TensorFlow constant folding does

    zero = builder.getFloatAttr(elem_type, 0);
  } else {
    return false;
  }
  if (auto ranked_type = dyn_cast<RankedTensorType>(type)) {
    llvm::ArrayRef<int64_t> type_shape = ranked_type.getShape();
    for (int64_t i : type_shape) {
      if (i < 0) return false;
    }
    shape = builder.getI64TensorAttr(type_shape);
  } else {
    return false;
  }
  return true;
}

        Please use input_signature or reduce_retracing to minimize retracing.

*   `tf.SparseTensor`:
    *   Introduced `set_shape`, which sets the static dense shape of the sparse tensor and has the same semantics as `tf.Tensor.set_shape`.

## Security

      ".getShape()[" # dim # " ] == " # size>]>;

// Returns true if the n-th operand is ranked and has a dimension length <=
// size at the rank dim.
class TFL_OperandDimIsAtMost<int n, int dim, int size> : And<[
  TFL_OperandIsRankedAndHasDimPred<n, dim>,
  CPred<"$_op.getOperand(" # n # ").getType().cast<ShapedType>()"
      ".getShape()[" # dim # " ] <= " # size>]>;

    llvm::ArrayRef<int64_t> shape_ref =
        mlir::cast<TensorType>(tensor_attr.getType()).getShape();
    if (mlir::failed(check_shape(shape_ref))) return std::nullopt;

    shape = std::vector<int32_t>(shape_ref.begin(), shape_ref.end());
  } else if (type.hasRank()) {
    llvm::ArrayRef<int64_t> shape_ref = type.getShape();
    if (mlir::failed(check_shape(shape_ref))) return std::nullopt;

  func.return %0 : tensor<?x?x?x?x?xf32>
  // CHECK-LABEL: conv3d_transpose
  // CHECK: %[[CST:.*]] = "tfl.no_value"() <{value}> : () -> none
  // CHECK: %[[OUT_SHAPE:.*]] = "tfl.cast"(%arg2) : (tensor<5xi64>) -> tensor<5xi32>

tensor.  The `input_indices` are recomputed based on the requested `new_shape`.

If one component of `new_shape` is the special value -1, the size of that
dimension is computed so that the total dense size remains constant.  At
most one component of `new_shape` can be -1.  The number of dense elements
implied by `new_shape` must be the same as the number of dense elements
originally implied by `input_shape`.