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	# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s \| FileCheck %s			# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s \| FileCheck %s

	import filecmp			import filecmp
	import numpy as np			import numpy as np
	import os			import os
	import sys			import sys
	import tempfile			import tempfile

	_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))			_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
	sys.path.append(_SCRIPT_PATH)			sys.path.append(_SCRIPT_PATH)

	from tools import mlir_pytaco_api as pt			from tools import mlir_pytaco_api as pt

	# Define the CSR format.			# Define the CSR format.
	#			csr = pt.format([pt.dense, pt.compressed], [0, 1])
	# TODO: accept "csr = pt.format([pt.dense, pt.compressed], [0, 1])"
	#
	csr = pt.format([pt.dense, pt.compressed])

	# Read matrices A and B from file, infer size of output matrix C.			# Read matrices A and B from file, infer size of output matrix C.
	A = pt.read(os.path.join(_SCRIPT_PATH, "data/A.mtx"), csr)			A = pt.read(os.path.join(_SCRIPT_PATH, "data/A.mtx"), csr)
	B = pt.read(os.path.join(_SCRIPT_PATH, "data/B.mtx"), csr)			B = pt.read(os.path.join(_SCRIPT_PATH, "data/B.mtx"), csr)
	C = pt.tensor((A.shape[0], B.shape[1]), csr)			C = pt.tensor((A.shape[0], B.shape[1]), csr)

	# Define the kernel.			# Define the kernel.
	i, j, k = pt.get_index_vars(3)			i, j, k = pt.get_index_vars(3)
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[mlir][sparse][pytaco] enable dimension ordering syntaxClosedPublic