in the flatbuffer.
  }];

  let arguments = (ins I32Attr:$buffer_index);

  let results = (outs AnyTensor:$output);
}

def TFL_Conv2DOp : TFL_ConvOp<"conv_2d", "Convolution", 0,
      [DeclareOpInterfaceMethods<InferTypeOpInterface>,
       DeclareOpInterfaceMethods<TFL_ArithmeticCount>,
       DynamicRangeQuantizedOpInterface]> {
  let arguments = (
    ins TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$input,

    to input arguments and outputs of the function(s).

    Two types of resources are supported:
    (1) A function argument of TF::ResourceType type (this pass).
    (2) A VarHandleOp in the function (tf-promote-var-handles-to-args).

    After the pass,

     . The function will have an input argument for each resource that is

</pre>

<p>
The type of the arguments and return value correspond.
For <code>complex</code>, the two arguments must be of the same
floating-point type and the return type is the complex type
with the corresponding floating-point constituents:
<code>complex64</code> for <code>float32</code> arguments, and
<code>complex128</code> for <code>float64</code> arguments.

    return op.emitOpError()
           << "input tensor argument is " << input_tensor_shape.size()
           << " which is invalid, since input tensor argument must has a "
           << "rank greater than 2.\n";
  }

  if (kernel_tensor_shape.size() <= 2) {
    return op.emitOpError()
           << "kernel tensor argument is " << kernel_tensor_shape.size()

// flag may be used with or without -m.
//
// The arguments to list -m are interpreted as a list of modules, not packages.
// The main module is the module containing the current directory.
// The active modules are the main module and its dependencies.
// With no arguments, list -m shows the main module.
// With arguments, list -m shows the modules specified by the arguments.
// Any of the active modules can be specified by its module path.

	}
	return abs
}

// passLongArgsInResponseFiles modifies cmd such that, for
// certain programs, long arguments are passed in "response files", a
// file on disk with the arguments, with one arg per line. An actual
// argument starting with '@' means that the rest of the argument is
// a filename of arguments to expand.
//
// See issues 18468 (Windows) and 37768 (Darwin).

  int func_arg_count = function_ty.getNumInputs();
  int arg_count = op.getArgs().size();

  if (arg_count != func_arg_count) {
    return op.emitError() << "argument count mismatch: 'args' has " << arg_count
                          << " arguments, but '" << func << "' expects "
                          << func_arg_count;
  }

  return success();
}

LogicalResult PartitionedCallOp::verifySymbolUses(

  // operation before the dst operation.
  Status AddBackedge(mlir::Operation* sink, mlir::Operation* dst,
                     int dst_input);

  // Adds the input arguments and return operation to the function. The
  // arguments are added as basic block argument. Also the argument types and
  // the id of the nodes from the input graph needs to be specified.
  Status ConvertFunctionArgAndRets(

			panic("reflect: CallSlice with too few input arguments")
		}
		if len(in) > n {
			panic("reflect: CallSlice with too many input arguments")
		}
	} else {
		if isVariadic {
			n--
		}
		if len(in) < n {
			panic("reflect: Call with too few input arguments")
		}
		if !isVariadic && len(in) > n {
			panic("reflect: Call with too many input arguments")
		}
	}
	for _, x := range in {

  if (op.getCond().front().getNumArguments() !=
      op.getBody().front().getNumArguments())
    return op.emitOpError(llvm::formatv(
        "number of arguments in condition function does not match number of "
        "arguments in body function ({0} != {1})",
        op.getCond().front().getNumArguments(),
        op.getBody().front().getNumArguments()));
  // Verify shapes are compatible.