input-group-text input[type=checkbox],.input-group-text input[type=radio]{margin-top:0}.input-group-lg>.custom-select,.input-group-lg>.form-control:not(textarea){height:calc(1.5em + 1rem + 2px)}.input-group-lg>.custom-select,.input-group-lg>.form-control,.input-group-lg>.input-group-append>.btn,.input-group-lg>.input-group-append>.input-group-text,.input-group-lg>.input-group-prepend>.btn,.input-group-lg>.input-group-prepend>.input-group-text{padding:.5rem 1rem;font-size:1.25rem;line-height:1.5;...

}

// -----

func.func @testSplitNonConstSplitDim(%input: tensor<4x4xf32>, %split_dim: tensor<i32>) {
  %0:2 = "tf.Split"(%split_dim, %input) : (tensor<i32>, tensor<4x4xf32>) -> (tensor<*xf32>, tensor<*xf32>)
  func.return
}

func.func @testSplitUnknownRankSplitDim(%input: tensor<4x4xf32>, %split_dim: tensor<*xi32>) {
  %0:2 = "tf.Split"(%split_dim, %input) : (tensor<*xi32>, tensor<4x4xf32>) -> (tensor<*xf32>, tensor<*xf32>)

      return op.emitOpError(
          "has mismatched quantized axes of input and output");
    }
  }

  return success();
}

static void BuildTransposeOp(OpBuilder* builder, OperationState& result,
                             Value input, Value perm) {
  // Output size is only known if input is ranked and perm is a constant.
  auto input_type = input.getType().cast<TensorType>();
  DenseIntElementsAttr perm_const;

  return ExpandTo4DForConvImpl(a, true);
}

TypeAttr RescaleQtype(Type input, Attribute factor) {
  return quant::RescaleQuantizedType(input, factor);
}

// Returns `true` if reducing `axes` in `input` with `keep_dims=true` results in
// the specified `shape` and `false` otherwise.
static bool ShapeMatchesReduceWithKeepAxes(Value input,
                                           const mlir::Attribute &axes,

  // If shape attribute equals input operand's type's shape, fold it to input.
  std::optional<llvm::ArrayRef<int64_t>> shape_constraint = getShape();
  if (type.getShape() == shape_constraint && getInput().getType() == getType())
    return getInput();

  // If input operand's type's shape always satisfies the shape attribute, fold
  // it to input.
  if (shape_constraint.has_value() &&

// Verifies that,
//
// - input has at least rank 1
// - multiples is rank 1
// - multiples.size() == input.rank()
// - input.rank() == output.rank()
// - Elements in multiples are non-negative
// - input.shape[i] * multiples[i] == output.shape[i]
//   for i in [0, input.rank() - 1]

LogicalResult TileOp::verify() {
  TileOp op = *this;

 * Executor)} on the input step.
 *
 * <h4>Catching</h4>
 *
 * To derive the next step from a failed input step, call {@link #catching(Class, ClosingFunction,
 * Executor)} or {@link #catchingAsync(Class, AsyncClosingFunction, Executor)} on the input step.
 *
 * <h4>Combining</h4>
 *
 * To derive a {@code ClosingFuture} from two or more input steps, pass the input steps to {@link

  state.operands.reserve(num_operands + 1);
  for (int input = 0, e = num_operands + 1; input != e; ++input) {
    if (input < dst_input) {
      state.operands.push_back(dst->getOperand(input));
    } else if (input == dst_input) {
      state.operands.push_back(source->getResult(0));
    } else {
      state.operands.push_back(dst->getOperand(input - 1));
    }
  }

 * Executor)} on the input step.
 *
 * <h4>Catching</h4>
 *
 * To derive the next step from a failed input step, call {@link #catching(Class, ClosingFunction,
 * Executor)} or {@link #catchingAsync(Class, AsyncClosingFunction, Executor)} on the input step.
 *
 * <h4>Combining</h4>
 *
 * To derive a {@code ClosingFuture} from two or more input steps, pass the input steps to {@link

	}
	// iterate through the above set of inputs and upload the object.
	for i, input := range putObjectInputs {
		// uploading the object.
		_, err := obj.PutObject(context.Background(), input.bucketName, input.objectName, mustGetPutObjReader(t, bytes.NewReader(input.textData), input.contentLength, input.metaData[""], ""), ObjectOptions{UserDefined: input.metaData})
		// if object upload fails stop the test.
		if err != nil {