SmallVector<int32_t> shape;
  shape.reserve(output_shape.size());

  bool needs_truncation = true;
  for (size_t dim_idx = 0; dim_idx < output_shape.size(); ++dim_idx) {
    int64_t dim = output_shape[dim_idx];
    if (truncate && needs_truncation && dim == 1) {
      continue;
    } else if (needs_truncation && dim != 1) {
      needs_truncation = false;
    }

    for (int64_t dim_idx = 0; dim_idx < value_rank; ++dim_idx) {
      int64_t dim_size = value_type.getDimSize(dim_idx);
      if (dim_size == ShapedType::kDynamic) {
        Value dim_i = rewriter.create<arith::IndexCastOp>(
            loc, shape_scalar_type,
            rewriter.create<tensor::DimOp>(loc, op.getOperand(), dim_idx));
        end_indices.push_back(dim_i);
        if (dim_idx != axis) {

  auto value_shape = value_shape_type.getShape();
  SmallVector<int32_t> return_squeeze_shape;
  return_squeeze_shape.reserve(value_shape.size());

  for (size_t dim_idx = 0; dim_idx < value_shape.size(); ++dim_idx) {
    int64_t dim = value_shape[dim_idx];
    if (dim == 1) {
      continue;
    }
    return_squeeze_shape.push_back(
        ShapedType::isDynamic(dim) ? -1 : static_cast<int32_t>(dim));
  }

  }

  const int64_t rank = input_type.getRank();

  IntegerAttr dim_int = ExtractSingleElementAsInteger(split_dim_attr);

  // "axis" operand could be a i64 tensor. Resolve it here.
  IntegerAttr dim_i32;
  if (failed(ConvertToI32Attr(dim_int, &dim_i32))) return failure();

  int dim = dim_i32.getInt();
  if (dim < 0) dim += rank;

  const SmallVector<Value, 4>& slice_outputs = SliceOutputs(

// Return the number of dimensions that the tensor has.
TF_CAPI_EXPORT extern int TF_NumDims(const TF_Tensor*);

// Return the length of the tensor in the "dim_index" dimension.
// REQUIRES: 0 <= dim_index < TF_NumDims(tensor)
TF_CAPI_EXPORT extern int64_t TF_Dim(const TF_Tensor* tensor, int dim_index);

// Return the size of the underlying data in bytes.
TF_CAPI_EXPORT extern size_t TF_TensorByteSize(const TF_Tensor*);

      int64_t dim_index = dim_index_attr.getValues<APInt>()[0].getSExtValue();

      int64_t rank = operand_ty.getRank();
      if (dim_index < 0 || dim_index >= rank) {
        return emitOptionalError(location, "dim_index (", dim_index,
                                 ") is out of range [0, ", rank, ")");
      }
      shape[dim_index] = ShapedType::kDynamic;
    } else {

// so as to find the right compilation unit.
func (ex *Examiner) FileRef(dw *dwarf.Data, dieIdx int, fileRef int64) (string, error) {

	// Find the parent compilation unit DIE for the specified DIE.
	cuDie := ex.ParentCU(dieIdx)
	if cuDie == nil {
		return "", fmt.Errorf("no parent CU DIE for DIE with idx %d?", dieIdx)
	}
	// Construct a line reader and then use it to get the file string.
	lr, lrerr := dw.LineReader(cuDie)

      dims, num_dims);
}

int TF_NumDims(const TF_Tensor* t) { return t->tensor->NumDims(); }

int64_t TF_Dim(const TF_Tensor* t, int dim_index) {
  return t->tensor->Dim(dim_index);
}

size_t TF_TensorByteSize(const TF_Tensor* t) { return t->tensor->ByteSize(); }

void* TF_TensorData(const TF_Tensor* t) { return t->tensor->Data(); }

			if err = checkCrossDeviceMounts(eps); err != nil {
				return nil, setupType, config.ErrInvalidErasureEndpoints(nil).Msg(err.Error())
			}

			for diskIdx := range eps {
				eps[diskIdx].SetPoolIndex(poolIdx)
				eps[diskIdx].SetSetIndex(setIdx)
				eps[diskIdx].SetDiskIndex(diskIdx)
			}

			endpoints = append(endpoints, eps...)
		}

		if len(endpoints) == 0 {

    ), "overridden configs should not include the old value"
    assert (
        '"syntaxHighlight": false' not in response.text
    ), "not used parameters should not be included"
    assert (
        '"dom_id": "#swagger-ui"' in response.text
    ), "default configs should be preserved"
    assert "presets: [" in response.text, "default configs should be preserved"
    assert (
        "SwaggerUIBundle.presets.apis," in response.text