parameters: (device, n, m, k, transpose_a, transpose_b, dtype)
docstring: Build a graph containing a sequence of matmul operations.
  
    Args:
      device: String, the device to run on.
      n: tensor A's first dimension size.
      m: tensor A's second dimension size.
      k: tensor B's second dimension size.
      transpose_a: boolean value to show if tensor A is transposed.
      transpose_b: boolean value to show if tensor B is transposed.
      dtype: numpy data type of the input tensor.
  
    Returns:
      A matmul operation to run()
docstring_summary: Build a graph containing a sequence of matmul operations.
docstring_tokens: Build a graph containing a sequence of matmul operations .
function: def build_graph(device, n, m, k, transpose_a, transpose_b, dtype):
    """Build a graph containing a sequence of matmul operations.
  
    Args:
      device: String, the device to run on.
      n: tensor A's first dimension size.
      m: tensor A's second dimension size.
      k: tensor B's second dimension size.
      transpose_a: boolean value to show if tensor A is transposed.
      transpose_b: boolean value to show if tensor B is transposed.
      dtype: numpy data type of the input tensor.
  
    Returns:
      A matmul operation to run()
    """
    with ops.device('%s' % device):
      if not transpose_a:
        x = variables.VariableV1(random_ops.random_uniform([n, m], dtype=dtype),
                                 use_resource=False)
      else:
        x = variables.VariableV1(random_ops.random_uniform([m, n], dtype=dtype),
                                 use_resource=False)
      if not transpose_b:
        y = variables.VariableV1(random_ops.random_uniform([m, k], dtype=dtype),
                                 use_resource=False)
      else:
        y = variables.VariableV1(random_ops.random_uniform([k, m], dtype=dtype),
                                 use_resource=False)
  
      z = math_ops.matmul(x, y, transpose_a=transpose_a, transpose_b=transpose_b)
      return control_flow_ops.group(z)
function_tokens: def build_graph ( device , n , m , k , transpose_a , transpose_b , dtype ) : with ops . device ( '%s' % device ) : if not transpose_a : x = variables . VariableV1 ( random_ops . random_uniform ( [ n , m ] , dtype = dtype ) , use_resource = False ) else : x = variables . VariableV1 ( random_ops . random_uniform ( [ m , n ] , dtype = dtype ) , use_resource = False ) if not transpose_b : y = variables . VariableV1 ( random_ops . random_uniform ( [ m , k ] , dtype = dtype ) , use_resource = False ) else : y = variables . VariableV1 ( random_ops . random_uniform ( [ k , m ] , dtype = dtype ) , use_resource = False ) z = math_ops . matmul ( x , y , transpose_a = transpose_a , transpose_b = transpose_b ) return control_flow_ops . group ( z )